# Enhancing Debris Flow Warning via Machine Learning Feature Reduction and Model Selection

Qi Zhou, Hui Tang, Clément Hibert, Malgorzata Chmiel, Fabian Walter, Michael Dietze, Jens M. Turowski

► To cite this version:

Qi Zhou, Hui Tang, Clément Hibert, Malgorzata Chmiel, Fabian Walter, et al. Enhancing Debris Flow Warning via Machine Learning Feature Reduction and Model Selection. Journal of Geophysical Research: Earth Surface, 2025, 130, 10.1029/2024JF008094. insu-05082245

HAL Id: insu-05082245

https://insu.hal.science/insu-05082245v1

Submitted on 23 May 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# JGR Earth Surface

## RESEARCH ARTICLE

10.1029/2024JF008094

**Key Points:**

* We compare the performance of three machine learning models and eighty seismic features in five sets to enhance debris flow early warning

* The number and even the types of seismic features are not critical for training an effective binary classifier for debris flow

* Long Short-Term Memory performs like ensemble models in classification task but it excels in predicting debris flow probability for warning

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

Q. Zhou,
qi.zhou@gfz.de;
qi.zhou.geo@gmail.com

**Citation:**

Zhou, Q., Tang, H., Hibert, C., Chmiel, M., Walter, F., Dietze, M., & Turowski, J. M. (2025). Enhancing debris flow warning via machine learning feature reduction and model selection. *Journal of Geophysical Research: Earth Surface*, 130, e2024JF008094. https://doi.org/10.1029/2024JF008094

Received 23 OCT 2024
Accepted 7 APR 2025

**Author Contributions:**

**Conceptualization:** Qi Zhou, Hui Tang, Jens M. Turowski

**Data curation:** Malgorzata Chmiel, Fabian Walter

**Methodology:** Qi Zhou, Clément Hibert, Malgorzata Chmiel, Michael Dietze

**Supervision:** Hui Tang, Michael Dietze, Jens M. Turowski

**Writing – original draft:** Qi Zhou

**Writing – review & editing:** Qi Zhou, Hui Tang, Clément Hibert, Malgorzata Chmiel, Fabian Walter, Michael Dietze, Jens M. Turowski

© 2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_2.png" /></figure>

## Enhancing Debris Flow Warning via Machine Learning Feature Reduction and Model Selection

Qi Zhou¹,² ID, Hui Tang¹ ID, Clément Hibert³ ID, Malgorzata Chmiel⁴, Fabian Walter⁵, Michael Dietze¹,⁶ ID, and Jens M. Turowski¹ ID

¹GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, ²Institute of Geosciences, University of Potsdam, Potsdam, Germany, ³Institut Terre et Environnement de Strasbourg / ITES, CNRS and University of Strasbourg, Strasbourg, France, ⁴Université Côte d'Azur, Observatoire de La Côte d'Azur, CNRS, IRD, Valbonne, France, ⁵Swiss Federal Institute for Forest, Snow and Landscape Research, Zürich, Switzerland, ⁶Faculty of Geoscience and Geography, Georg-August-Universität Göttingen, Göttingen, Germany

**Abstract** The advent of machine learning has significantly improved the accuracy of identifying mass movements through the seismic waves they generate, making it possible to implement real-time early warning systems for debris flows. However, we lack a profound understanding of the effective seismic features and the limitations of different machine learning models. In this work, we investigate eighty seismic features and three machine learning models for single-station-based binary debris flow classification and multi-station-based warning tasks. These seismic features, derived from physical and statistical knowledge of impact sources, are grouped into five sets: Benford's law, waveform, spectra, spectrogram, and network. The machine learning models belong to two families: two ensemble models, Random Forest and eXtreme Gradient Boosting (XGBoost); one recurrent neural network model, Long Short-Term Memory (LSTM). We analyzed feature importance from the ensemble models and found that the number and even the types of seismic features are not critical for training an effective binary classifier for debris flow. When using models designed to capture patterns in sequential data rather than focusing on information only in one given window, using the LSTM does not significantly improve the performance of binary debris flow classification task over Random Forest and XGBoost. For the multi-station-based debris flow warning task, the LSTM model predicts debris flow probability more consistently and provides longer warning times. Our proposed framework simplifies machine learning-driven debris flow classification and lays the foundation for affordable seismic signal-driven early warning using a sparse seismic network.

**Plain Language Summary** Machine learning can enhance the accuracy of detecting debris flow, landslides, and rockfall through seismic signals and add more time to warn people. However, it remains to be seen which machine learning models and seismic features are the most effective, especially to identify debris flows. In this study, we utilized two machine learning models, the Random Forest model and the eXtreme Gradient Boosting model (XGBoost), to evaluate the importance of seismic features in distinguishing debris flows from other events of interest. Our study found that using over seventy seismic features to train a machine learning model to classify debris flows includes unnecessary input features. The number and even the types of seismic features are not critical for training an effective binary classifier for debris flow. We also experimented with a different algorithm, Long Short-Term Memory (LSTM), but it did not improve classification performance compared to Random Forest and XGBoost. The LSTM was more effective in detecting the onset of debris flows and producing fewer false warnings for alarms. Our approach simplifies the use of seismic signals to build early warning systems and may be applied to other types of mass movement events as well.

## 1. Introduction

Debris flows are a widespread gravity-driven hazard in mountainous regions. These mixtures of water, mud, and solid particles of various sizes (Hutter et al., 1994; Iverson, 1997) can be triggered by shallow landslides, runoff onto unconsolidated soil, in-channel mobilization, dam failure, seismic liquefaction, and rapid snow melting (Jakob et al., 2005; Takahashi, 2007). Due to their high impact force, intense basal-lateral erosion, and long outflow distances, debris flows are one of the most destructive mass movements in the mountain landscapes and pose a threat to residents and property along the main flow path (Badoux et al., 2009; Santi et al., 2011). On a global scale, at least 77,779 fatalities and billions of dollars of socioeconomic damage were caused by debris
flows from 1950 to 2011 (Dowling & Santi, 2014). For example, on 17 July 2023, a devastating debris flow in Quetame, Colombia, caused 14 deaths and left 20 people missing (Petley, 2023). Similarly, on 21 June 2024, a sudden debris flow in Sorte, Switzerland, resulted in the deaths of three people (Petley, 2024). It is still challenging to ensure the safety of life and property within the debris-flow hazard zone.

Early warning systems offer a cost-effective approach for reducing debris flow risk (Hürlimann et al., 2019). The type of sensors used for data collection typically determines the maintenance effort and promotion of the methodology for an early warning system setup (Arattano & Marchi, 2008; Badoux et al., 2009). Environmental seismology focuses on investigating seismic signals generated by Earth's surface dynamics (Cook & Dichtze, 2022; Larose et al., 2015). It offers a novel, non-invasive method for monitoring mass movements with high temporal resolution. And seismic waves travel approximately a hundred times faster than the propagation of mass movements, enabling the registration of early warning in advance (Aki & Richards, 2002; Burtin et al., 2014). On the catchment scale, gravity-driven excitation sources, such as debris flows, glacial lake outburst floods, and lahars, run along channels and create a time delay between seismic waves and themselves to issue warning and rescue (Cook et al., 2021). In a non-contact manner, a seismic network can capture the footprints of debris flows as they evolve and travel through a channel (Lai et al., 2018), and these footprints are essential for building early warning systems. Even though rainfall-based debris flow warning systems can issue a warning up to 3 hours or a few days in advance (Tiranti et al., 2014), precipitation is considered one of the triggers rather than a mandatory condition for debris flow. In contrast, seismic signal-driven early warning is based on monitoring the debris flow dynamic process and focuses more on the post-initiation stages. Depending on catchment size and debris flow front velocity, the seismic signal-driven warning systems can issue warning times ranging from several minutes to 3 hours (Badoux et al., 2009; Chmiel et al., 2021; Hübl et al., 2013). However, maximizing the true warnings while minimizing the false warnings based on single-station signals to issue final alarms at the catchment scale remains demanding.

Machine learning methods offer a promising approach to handling massive data sets and mining hidden information (Bergen et al., 2019; Dannemann Dugick et al., 2023). Setting up a machine learning-driven debris flow warning system using time series data is usually considered a classification task. The model classifies the time stream within a given time window as a *debris flow* (or *warning*) label or *other* (or *no warning*) label. Supervised machine learning models, such as Support Vector Machines and decision-tree-based ensemble models (e.g., Random Forest or eXtreme Gradient Boosting), have shown promise in classifying debris flows from time series data, such as rainfall and seismic signals (Ponziani et al., 2023; Zhao et al., 2022). In practice, previous works, such as Chmiel et al. (2021); Hibert et al. (2017); Wenner et al. (2020), have shown that training the machine learning-driven mass movements classification model needs to employ many seismic features that are derived from both physical and statistical knowledge of impact sources. These features include temporal features in the waveform, frequency features in the spectra, evolution of frequency-time features in the spectrogram, and seismic network based metrics (Provost et al., 2017). Decision tree-based algorithms offer a degree of model interpretability and can provide feature importance to inform model retraining or refinement (Breiman, 2001; Chen & Guestrin, 2016; X. Zhou et al., 2021). This is particularly valuable because: (a) including irrelevant features during model training can lead to overfitting and diminished generalization capabilities; and (b) incorporating prior knowledge can enable machine learning models to converge faster and train more efficiently (John et al., 1994; Kotsiantis, 2011). For example, Provost et al. (2017) and Chmiel et al. (2021) showed features based on seismic networks are critical for classifying mass movements from background noise. However, many debris flow catchments, which face harsh environmental conditions and require warning systems, lack the resources to maintain or establish dense seismic networks. The previous workflow poses an uncertainty to the reproducibility and transferability of the trained model in different real-world scenarios, especially if the new catchment has a different morphology or a different seismic network configuration (e.g., number of stations and inter-station distances). Consequently, it remains unclear whether utilizing the network features is truly necessary for building a reliable early warning system.

Meanwhile, seismic-driven debris flow warning is not strictly a classification or regression task, as it requires the model to provide more precise timing or predictions as early as possible for debris flow initiation rather than the entire period. Decision tree-based models cannot directly capture temporal patterns or information inherent in continuous time series data. However, these temporal signatures of debris flows in seismic signals are critical for the design of warning systems. Hidden Markov Models can employ memory-like functions to detect volcanic events and slope-failure events from seismic signals (Hammer et al., 2012; Lagarde et al., 2023).
However, the assumption that state transitions depend only on the current state (not on any past state) is not always satisfied and may hinder the application of Hidden Markov Models (Abraham et al., 2022). In addition, recurrent neural networks and temporal convolutional network model are well-suited for this challenge because these machine learning algorithms can remember historical patterns and apply them to the current status (Lea et al., 2017; Sherstinsky, 2020). So far, these methods have yet to be fully tested for debris flow early warning purposes.

While the system proposed by Chmiel et al. (2021) is well-suited for the Illgraben catchment, utilizing six seismic stations, it is important to design a more flexible and feasible alternative for other catchments with more common, but sparser, seismic network configurations (e.g., one or two seismic stations), and to decouple the warning workflow. In such cases, a *simpler is better* design philosophy has proven to work better (Hürlimann et al., 2019). In this work, we modularize the workflow of a seismic-driven debris flow early warning system into four key steps: (a) data collection and transmission, (b) single-station-based classification or detection, (c) multi-station-based warning issuance, and (d) information communication and response. Step (b) treats the time series seismic data as distinct discrete time windows, analyzing each sub-time window equally to develop a binary debris flow classifier. Step (c) involves the warning strategy, focusing on issuing timely alerts based on the predicted debris flow probabilities from multiple stations generated in Step (b), with the aim of providing the earliest possible warning and less false warning. This approach enables the trained model from step (b), regardless of whether it incorporates features based on seismic network, to be transferred to a different catchment, while step (c) needs to be adapted to the specific characteristics of the new catchment.

In this study, we focus on the single-station-based binary debris flow classification task and discuss the multi-station-based warning task. We propose an early warning strategy that combines information from multiple seismic stations to issue the final warning. The objective is to investigate the combinations of seismic features (eighty seismic features in five sets) and machine learning models (two ensemble models, Random Forest and eXtreme Gradient Boosting; one recurrent neural network structured model, Long Short-Term Memory (LSTM)) that yield the highest performance for classification and warning tasks. We validate our findings against previously published results for the multi-station-based warning task (Chmiel et al., 2021). Seismic data, including 32 debris flow events, were collected from a network in the Illgraben catchment, Switzerland, between 2017 and 2020.

## 2. Study Site and Debris Flow Catalog

### 2.1. Site Overview

We used seismic data from Illgraben catchment in Switzerland (Figure 1a). The Illgraben catchment is one of the most active debris flow basins in the European Alps, subject to several rainfall-triggered debris flows and floods each year (McArdell, 2016; McArdell et al., 2007). Together with the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), the Swiss Seismological Service at ETH Zurich has deployed a temporary seismic network, coded as 9S in the International Federation of Digital Seismograph Networks (FDSN), to monitor mass movements in Illgraben (Zurich, 2012). The network is equipped with several broadband seismometers, and its instrumentation specifications are listed in Table S1 in Supporting Information S1. Furthermore, several check dams (abbreviated as CD) were constructed in the 1960s to stabilize the debris flow channel and minimize bottom and lateral erosion (Figure 1a). For example, on the upstream check dam CD1, a geophone plate was installed to measure vertical ground velocity and provide a time stamp when a debris flow passed over it. The CD29 is equipped with an impact force plate to measure vertical force and also provides a time stamp when debris flow reaches the Rhône River. In 2007, the local government commissioned and implemented a warning system to mitigate the debris flow hazards (Badoux et al., 2009). A machine learning model, using a Random Forest algorithm, has been reported to provide additional warning time between 20 min and over 1.5 hr with respect to CD29 (Chmiel et al., 2021). For more information on the Illgraben and local debris flow warning systems, readers can refer to the works of Badoux et al. (2009), Burtin et al. (2014), and Chmiel et al. (2021).

### 2.2. Debris Flow Catalog

We selected open-access seismic data from the period 2017 to 2020. The debris flow observatory station of WSL recorded 32 debris flow events: five in 2017, four in 2018, eleven in 2019 (excluding 2019-10-09 and 2019-10-15, as data for ILL18 is not available), and twelve in 2020 (Chmiel et al., 2021). We observed that applying previously
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Site map of the Illgraben catchment and debris-flow physical parameters. (a) Illgraben catchment and distribution of seismic stations. The stations utilized in this paper are indicated in red. (b) Debris flow volume and front velocity between 2017 and 2020. Only the events with observed data are shown here. Data source: McArdell and Hirschberg (2020); McArdell et al. (2023).</figcaption></figure>

trained machine learning models to seismic data from different networks can be challenging due to variations in station number and distribution. In this work, to ensure the reproducibility of our workflow for other catchments with varying seismic network distributions, we employed a network with a sparse, linear, close-to-channel distribution—a common strategy for monitoring linear source mass movements such as debris flows. Due to intermittent failures of the ILL17 and ILL16 stations (e.g., in May 2017, when two debris flow events were observed), the ILL18 station (primarily monitoring the debris flow initiation period), ILL12 station (closest to the channel), and ILL13 station (primarily monitoring the debris flow run-out outside the catchment) were selected, spanning from upstream to downstream in the Illgraben (Figure 1a). The three selected stations, arranged in a roughly linear distribution from upstream to downstream, represent the most common and straightforward approach for installing seismic sensors to monitor debris flows. The available field data shows that debris flow events have volumes ranging from $3.3 \times 10^3$ to $1 \times 10^5$ m³ and front velocities ranging from 0.6 to 7.1 m/s (Figure 1b). The debris-flow front velocity was estimated based on the event front detection with geophones at CD29 near Illgraben's confluence with the Rhône River and CD28 (around 140 m upstream of CD29), and debris flow volume was calculated based on the flow depth measured by a radar device at CD29 (McArdell et al., 2023). The reader is referred to Chmiel et al. (2021); McArdell and Hirschberg (2020); McArdell et al. (2023) for more details on the physical characteristics of the debris flow events.

# 3. Methods

## 3.1. Labeling Seismic Data

Following the methodology described by Burtin et al. (2014, 2016), Belli et al. (2022), we manually labeled the start time $t_s$ and end time $t_e$ of each debris flow at stations ILL18, ILL12, and ILL13 by referring to the vertical component waveform and spectrogram and the time stamps when the debris flow crossed CD1. Details of the labeling method can be found in Text S1 of Q. Zhou et al. (2024). The continuous seismic data were sliced into one-minute non-overlapping equal-length windows, with each window assigned a binary label (debris flow or no debris flow). The debris flow label (DF) was defined as the timestamp (or time period) $t_i$ between the manually labeled start time $t_s$ and end time $t_e$, while the no debris flow label (Non-DF) was defined as the time outside the manually labeled $t_s$ and $t_e$. The Non-DF label may include time periods associated with events such as earthquakes, rockfalls, vehicle movements, human activities, and local background noise. The $t_s$ and $t_e$ of each debris flow are listed in Table S2 in Supporting Information S1, and the numbers of DF and Non-DF labels for the three stations are provided in Table 1.
<table><caption>**Table 1**
Number of Manually Labeled Debris Flow (DF) and None Debris Flow (Non-DF)</caption><thead><tr><th rowspan="3">Station</th><th colspan="8">Year</th></tr><tr><th colspan="2">2017</th><th colspan="2">2018</th><th colspan="2">2019</th><th colspan="2">2020</th></tr><tr><th colspan="2">Five events</th><th colspan="2">Four events</th><th colspan="2">Eleven events</th><th colspan="2">Twelve events</th></tr><tr><th></th><th>DF label</th><th>Non-DF label</th><th>DF label</th><th>Non-DF label</th><th>DF label</th><th>Non-DF label</th><th>DF label</th><th>Non-DF label</th></tr></thead><tbody><tr><td>ILL18</td><td>335</td><td>65,905</td><td>230</td><td>138,010</td><td>720</td><td>137,520</td><td>1,040</td><td>144,400</td></tr><tr><td>ILL12</td><td>310</td><td>65,930</td><td>233</td><td>138,007</td><td>937</td><td>137,303</td><td>1,320</td><td>144,120</td></tr><tr><td>ILL13</td><td>310</td><td>65,930</td><td>208</td><td>138,032</td><td>1,329</td><td>136,911</td><td>1,335</td><td>144,105</td></tr></tbody></table>

Note. The number of events indicates how many debris flows were selected, and the number of labels indicates how many one-minute non-overlapping windows were used.

## 3.2. Selecting Seismic Features for Machine Learning Model

Seismic signals can be described by a multitude of features, including waveform, spectra, spectrogram, and seismic network based metrics. These features are derived from physical and statistical knowledge of the impact sources and are introduced to train machine learning models to classify rockfalls, earthquakes, and landslides (Hibert et al., 2017; Provost et al., 2017; Wenner et al., 2020). Chmiel et al. (2021) extended these four sets of features to debris flow classification in the Illgraben catchment. Moreover, several seismic features related to Benford's law (also known as the first-digit law) have been demonstrated to effectively detect mass movement (Newcomb, 1881; Sambridge et al., 2010; Q. Zhou et al., 2024). In this study, we extracted eleven features related to Benford's law (Figure 3), including the first digit frequency of one to nine, the goodness of fit, and the power law exponent. We designated these features as Type A (Benford's law set) and provided the definition and calculating equation in Text S1 in Supporting Information S1 (Feature IDs 0–10). Except for these, following Chmiel et al. (2021), we excluded one feature, the number of active stations among the seismic network (always equal to three in this work), and selected 69 out of the 70 seismic features, naming them the Type B features (waveform, spectra, spectrogram, and seismic network based metrics set). For the definition and calculated equation of Type B features, readers can refer to Text S1 in Supporting Information S1 (Feature IDs 11–79) and Provost et al. (2017). The combination of Types A (Benford's law) and B (waveform, spectra, spectrogram, and seismic network based metrics) yielded a further Type C (Figure 3). To focus on the seismic signal generated by debris flow, we processed the vertical component seismic signals by removing sensor response, demeaning, detrending, and bandpass filtering between 1 and 45 Hz. The continuous seismic data were then also sliced using a one-minute non-overlapping moving window to compute the 80 seismic features and assign labels as described in Section 3.1. The details of the workflow are provided in Figure S1 in Supporting Information S1.

## 3.3. Supervised Machine Learning Model

The following two points should be considered when selecting machine learning models for debris-flow early warning: (a) model interpretability, or the ability to provide insights into the contribution of seismic features to the binary debris flow classification task, and (b) the capacity to capture sequential information, enabling recognition and understanding of the temporal sequence of debris flows for warning task. In this work, three machine learning models were selected: two interpretable decision tree-based ensemble models, the Random Forest model (RF) and the eXtreme Gradient Boosting model (XGBoost), and a LSTM recurrent neural network model for its ability to capture sequence information.

The Random Forest (RF) model is a supervised machine learning algorithm based on the concept of bagging and decision trees (Breiman, 2001). It builds on multiple independent and parallel decision trees during the training process and combines the predictions of the trees by averaging for regression or voting for classification to produce the final output (Cutler et al., 2012). RF has been shown to work well for classifying seismic signals generated by earthquakes (Fernández-Delgado et al., 2014), as well as environmental sources such as rockfalls, landslides, and debris flows (Chmiel et al., 2021; Hibert et al., 2019; Provost et al., 2017; Wenner et al., 2020).

The eXtreme Gradient Boosting model (XGBoost) is also based on decision trees but uses the boosting framework (Chen & Guestrin, 2016). XGBoost builds decision trees sequentially, each tree correcting the errors made
by the previous ones and optimizing the construction of the tree to minimize the overall prediction error. The XGBoost model is generally found to be more efficient for training (e.g., parallelization and distributed computing) and frequently yields superior outcomes to the RF model for classification tasks (González et al., 2020). It has usually been applied to seismic signals generated by earthquakes rather than mass movements, such as seismic source discrimination and earthquake intensity estimation (Abdalzaher et al., 2021, 2022; Sho-kouhi et al., 2021).

The LSTM model is a typical recurrent neural architecture that is designed for sequential or time series (Hochreiter & Schmidhuber, 1997). It inherits the classical recurrent neural network ability to track the hidden patterns in time series while using a forget gate to avoid the vanishing gradient problem (Karim et al., 2017). LSTM is commonly used for tasks where the order of data points matters, such as speech recognition and time series prediction (Van Houdt et al., 2020). This property may make the LSTM model suitable for early warning tasks by tracking time series information during debris flow initiation. The amplitude and central frequency, which depend on the source-station distance as debris flows travel in the channel, may be effectively captured by the LSTM model. This model has shown promising results for onsite earthquake detection, earthquake magnitude estimation, and earthquake early warning (Bai & Tahmasebi, 2022; Cofré et al., 2022; Wang et al., 2022).

In our work, Random Forest and XGBoost were configured with 500 decision trees. The hyperparameter *class_weight* was set to the default value of one for Random Forest, and the *scale_pos_weight* was set to the default value of one for XGBoost. Additionally, the number of recurrent layers in the LSTM was set to four. For input feature importance, we employed Gini importance (also known as mean decrease in impurity) for Random Forest and average gain importance for XGBoost to calculate seismic feature importance in the single-station-based binary debris flow classification task (Breiman, 2001; Chen & Guestrin, 2016; Pedregosa et al., 2011). Both Random Forest and XGBoost models assign higher importance values to features that are more influential in generating binary debris flow classifications. To simplify the setup of the model hyperparameters, we adopted the default parameters of the open-access library Scikit-learn version 1.3.0 (Pedregosa et al., 2011), Distributed (Deep) Machine Learning Community XGBoost version 1.7.6 (Chen & Guestrin, 2016), and PyTorch version 2.0.1 (Paszke et al., 2019). Both Random Forest and XGBoost have built-in procedures to control overfitting to some extent (Chen & Guestrin, 2016; Maggi et al., 2017; Pedregosa et al., 2011). Apart from comparing training and test accuracy, we did not apply any additional methods to address the overfitting issue. For the LSTM model, we set the dropout probability to 0.1 on the outputs of the second-to-last layer, performed validation every epoch to prevent overfitting, and then saved the trained model parameters when the validation was optimal. For parameters not mentioned above, please refer to the code provided in the open research repository.

### 3.4. Model Training and Validation

We break down the workflow as follows: (a) model training, using all available data from 2017 to 2019 to train machine learning models for stations ILL18, ILL12, and ILL13, respectively; (b) model validation, using all available 2020 data to validate the trained model. To address the issue of imbalanced data, class weights of 0.1 were assigned to the Non-DF label (no debris flow) and 0.9 to the DF label (debris flow) during model training. For a detailed explanation and the underlying reasons, please refer to Section 5.1. Additionally, the impact of varying Non-DF to DF label ratios during training on model performance was analyzed and presented in the discussion section. To reduce the uncertainty, we repeat the training and validation process five times and present the mean results in the following part.

In the implementation, we followed the workflow and compared the validation results from the 27 combinations of feature types (A, B, and C), model types (RF, XGBoost, and LSTM), and seismic stations (ILL18, ILL12, and ILL13) for binary debris flow classification task and debris flow alarm task. To ensure the model is not inadvertently affected by local background noise and site-specific effects, each input seismic feature X was linearly scaled from zero to one by min-max normalization during training. The same $X_{max}$ and $X_{min}$ obtained from the training data will be used to scale the validation data as:

$$X_{\text{scaled}} = \frac{X - X_{\text{min}}}{X_{\text{max}} - X_{\text{min}}}, \qquad (1)$$

where X is the data set of a given input feature, $X_{max}$ and $X_{min}$ are the maximum and minimum values of X.
The single-station-based binary debris flow classification task treats each sub-time window equally over the selected period to develop a binary debris flow classifier. The machine learning model accepts features extracted from seismic signals recorded at a station within a one-minute, non-overlapping window and outputs the debris flow probability for that window. For a given time window at the time stamp $W_t$, any output probability Pr of debris flow exceeding 0.5 (the algorithm default threshold) will be considered indicative of debris flow (DF, $Pr \ge 0.5$); an output probability of debris flow below 0.5 will be interpreted as indicative of no debris flow (Non-DF, $Pr < 0.5$). The results are quantified with a confusion matrix (Text S2 in Supporting Information S1), which includes true positive (TP), true negative (TN), false positive (FP), and false negative (FN). We used the F1 score (F1) to elevate the model's performance (Equation S1 in Supporting Information S1). To evaluate whether there are significant differences among different combinations of machine learning models and feature types for the single-station-based binary debris flow classification task, we set the null hypothesis $H_0$ as: There is no significant difference between two different model-feature combinations. We then performed the non-parametric McNemar's test to assess this hypothesis (Rainio et al., 2024; Seabold & Perktold, 2010; Trajman & Luiz, 2008); details can be found in Text S2 and Table S4 in Supporting Information S1.

The multi-station-based warning task focuses on issuing timely warnings based on the predicted debris flow probabilities from multiple stations, aiming to provide the earliest possible warning while minimizing false alarms. The warning strategy accepts the predicted debris flow probabilities from different stations and then determines whether to issue a warning. Our warning strategy with three parameters: (a) the number of trained machine learning models predicted debris flow probability $n$ (which corresponds to the number of used stations, always three in this work), (b) the attention window size $l$, measured in minutes, which spans the time period from the previous window $W_{t-1}$ to the current window $W_t$, and (c) the global averaged probability $Pr_g$, derived from the $n$ trained machine learning models within the window size $l$. For a given time window at the time stamp $W_t$, our warning strategy analyzes a matrix of dimensions $(l, n)$ and issues a warning for $W_t$ if the average probability exceeds $Pr_g$. Warnings issued more than 30 min before the manually labeled start of the ILL18 event or more than 180 min after the CD29 time $t_{CD29}$ are classified as false warnings. We compared increased warning time ($\Delta_t$) between our first issued warning time stamp $W_t$ and the time stamp $t_{CD29}$ when a debris flow front reached the CD 29 (Equation 2). To assess the performance of our multi-station-based warning, we refer to the results from Chmiel et al. (2021) and we calculated the increased warning time for Pre-CD1 warning ($\Delta_{t1}$, debris flow before CD1 or early debris flow) and Post-CD1 warning ($\Delta_{t2}$, debris flow after CD1 or late debris flow) for their work (Equations 3 and 4). The time stamps of $W_{t1e}$, $W_{t2e}$, and $t_{CD29}$ are listed in Table 2:

$$ \Delta_t = t_{CD29} - W_t, \qquad (2) $$

$$ \Delta_{t1} = t_{CD29} - W_{t1e}, \qquad (3) $$

$$ \Delta_{t2} = t_{CD29} - W_{t2e}, \qquad (4) $$

# 4. Results

## 4.1. Binary Debris Flow Classification at Single Station

During the model training process using the 2017–2019 seismic data, the LSTM model trained with Type A feature underperforms than other model-feature combinations, with a mean F1 score of 0.638 across the three stations. This is because not all subwindows within the labeled debris flow periods follow Benford's Law (Q. Zhou et al., 2024), and the LSTM model struggles to learn the patterns of the early and late stages of debris flow periods. Except that, our results consistently showed the F1 to be greater than 0.746 with a mean value of 0.940 (Table S5 in Supporting Information S1). The validation results for the 2020 data demonstrated that the combinations of machine learning models and seismic features effectively perform the binary debris flow classification task, and the observed standard deviation across five repetitions is not significant (Table 3). Our models tended to mislabel the time windows near the manually labeled debris flow start time and end time as false negatives (Figure S2 in Supporting Information S1), and the false positives did not persist over time or seem to occur randomly. The validation results were quantified by F1 score. The bold F1 values indicate the maximum value model-feature combinations for each station. When calculating the difference between the F1 of training and validation for each model-feature type combination at the station level (indicating overfitting or not),
<table><caption>**Table 2**

*Earliest Warning Time in the 2020 Validation Benchmark*</caption><thead><tr><th>Debris flow index</th><th>Start time window W<sub>t1s</sub></th><th>End time window W<sub>t1e</sub></th><th>Warning type</th><th>Start time window W<sub>t2s</sub></th><th>End time window W<sub>t2e</sub></th><th>Warning type</th><th>CD29 time t<sub>CD29</sub></th></tr></thead><tbody><tr><td>E1</td><td>2020-06-04 14:53:28</td><td>14:55:08</td><td>Pre-CD1</td><td>15:30:08</td><td>15:31:48</td><td>Post-CD1</td><td>15:38:51</td></tr><tr><td>E2</td><td>2020-06-07 07:31:48</td><td>07:33:28</td><td>Pre-CD1</td><td>08:10:08</td><td>08:11:48</td><td>Post-CD1</td><td>09:49:32</td></tr><tr><td>E3</td><td>2020-06-08 13:41:48</td><td>13:43:28</td><td>Pre-CD1</td><td>14:23:28</td><td>14:25:08</td><td>Post-CD1</td><td>15:51:59</td></tr><tr><td>E4</td><td>2020-06-08 16:33:28</td><td>16:35:08</td><td>Pre-CD1</td><td>17:08:28</td><td>17:10:08</td><td>Post-CD1</td><td>17:59:10</td></tr><tr><td>E5</td><td>2020-06-09 23:35:08</td><td>23:36:48</td><td>Pre-CD1</td><td>N/A</td><td>N/A</td><td>N/A</td><td>01:05:43 (+1)</td></tr><tr><td>E6</td><td>2020-06-16 20:20:08</td><td>20:21:48</td><td>Pre-CD1</td><td>23:23:28</td><td>23:25:08</td><td>Post-CD1</td><td>23:56:18</td></tr><tr><td>E7a</td><td>2020-06-17</td><td>N/A</td><td>N/A</td><td>01:50:08</td><td>01:51:48</td><td>Post-CD1</td><td>04:06:58</td></tr><tr><td>E7b</td><td>2020-06-17 03:10:08</td><td>03:11:48</td><td>Pre-CD1</td><td>03:38:28</td><td>03:40:08</td><td>Post-CD1</td><td>04:06:58</td></tr><tr><td>E8</td><td>2020-06-29 04:31:49</td><td>04:33:29</td><td>Pre-CD1</td><td>05:08:29</td><td>05:10:09</td><td>Post-CD1</td><td>05:49:13</td></tr><tr><td>E9</td><td>2020-07-28 16:03:30</td><td>16:05:10</td><td>Pre-CD1</td><td>17:08:30</td><td>17:10:10</td><td>Post-CD1</td><td>17:58:55</td></tr><tr><td>E10</td><td>2020-08-16 21:13:28</td><td>21:15:08</td><td>Pre-CD1</td><td>21:50:08</td><td>21:51:48</td><td>Post-CD1</td><td>23:04:11</td></tr><tr><td>E11</td><td>2020-08-30 04:52:44</td><td>04:54:24</td><td>Pre-CD1</td><td>05:39:24</td><td>05:41:04</td><td>Post-CD1</td><td>05:52:23</td></tr></tbody></table>

*Note.* The Start Time Window and End Time Window correspond to the timestamps of seismic signals used as input for the benchmark model. Pre-CD1 and Post-CD1 represent the benchmark model's results for debris flow events occurring before and after CD1, respectively. N/A indicates no data. The event on 9 June 2020 may be a flood that stopped before it reached CD29. The same *t*<sub>CD29</sub> was used to calculate Δ for E7a and E7b. The reader is referred to Chmiel et al. (2021) for more details on their warning strategy. All times are in Coordinated Universal Time (UTC+0).

ensemble tree models tend to show a decline in performance, whereas the LSTM model tends to show an improvement in performance (Table S5 in Supporting Information S1). For example, the highest F1 drop among the two ensemble models across the three stations is 0.354 (observed in the Random Forest model trained with Type A features at the ILL18 station), and the XGBoost model shows a mean F1 drop of 0.137 across the nine station-feature combinations. In contrast, the LSTM model tends to improve the F1 by a mean value of 0.145 across the nine station-feature combinations. The LSTM model trained with Type C features demonstrates the most stable performance from training to validation across the selected three stations, with a mean increase of 0.097 and a maximum increase of 0.156.

Based on the hypothesis tests, we found that most pairs of model-feature combinations provide strong evidence against the null hypothesis $H_0$ (p-value ≤ 0.05), indicating that different combinations for binary classification do not perform equally (Figure 2 and Table S3 in Supporting Information S1). The LSTM model generally outperforms the Random Forest and XGBoost models for this binary classification task with same feature type, especially at the closest station ILL12 (Figure 2 and Table 3). Type B was more effective than Type A (Figures 2a–2c); for example, the maximum and average F1 improvements among the three models were 0.191 and 0.08 (observed at the ILL12 station trained with the Random Forest model) (Table 3), respectively. While Type C only slightly improved the performance of the machine learning models compared to Type B, the maximum increase in F1 score was 0.109, observed at the ILL18 station trained with the LSTM model (Table 3). It is hypothesized that the Type C features used for model training may be affected by redundant input information. The relationship between the number of features and model performance will be discussed in detail in Section 5.3.

## 4.2. Effects of Feature Weight

The reliability of feature weights was determined by the performance of two ensemble machine learning models in the binary debris flow classification task. Our results, based on five independent repetitions of the training and validation, showed that the Random Forest and XGBoost models trained with Type C features (ID 0–79) performed consistently well at each station for the classification task (Table S5 in Supporting Information S1 and Table 3). While the contribution of different seismic feature sets (Benford's law, waveform, spectra, spectrogram, and seismic network-based metrics) to the binary debris flow classification task varies significantly (Figure 3). We observed no significant differences in weight variance across all Type C features among the five repetitions.
<table><caption>**Table 3**

Validation Confusion Matrix for Combinations of Seismic Station, Machine Learning Model, and Training Feature</caption><thead><tr><th>Station</th><th>Model</th><th>Feature</th><th>TN</th><th>FP</th><th>FN</th><th>TP</th><th>F1</th></tr></thead><tbody><tr><td rowspan="12">ILL18</td><td rowspan="3">Random Forest</td><td>A</td><td>144,374 ± 2</td><td>26 ± 2</td><td>539 ± 5</td><td>501 ± 5</td><td>0.640 ± 0.004</td></tr><tr><td>B</td><td>144,383 ± 1</td><td>17 ± 1</td><td>334 ± 4</td><td>706 ± 4</td><td>0.801 ± 0.002</td></tr><tr><td>C</td><td>144,383 ± 0</td><td>17 ± 0</td><td>334 ± 2</td><td>706 ± 2</td><td>0.801 ± 0.001</td></tr><tr><td rowspan="3">XGBoost</td><td>A</td><td>144,254 ± 0</td><td>146 ± 0</td><td>373 ± 0</td><td>667 ± 0</td><td>0.720 ± 0.000</td></tr><tr><td>B</td><td>144,323 ± 0</td><td>77 ± 0</td><td>174 ± 0</td><td>866 ± 0</td><td>0.873 ± 0.000</td></tr><tr><td>C</td><td>144,325 ± 0</td><td>75 ± 0</td><td>178 ± 0</td><td>862 ± 0</td><td>0.872 ± 0.000</td></tr><tr><td rowspan="3">LSTM</td><td>A</td><td>144,294 ± 31</td><td>58 ± 31</td><td>133 ± 34</td><td>907 ± 34</td><td>0.905 ± 0.007</td></tr><tr><td>B</td><td>144,061 ± 47</td><td>291 ± 47</td><td>89 ± 27</td><td>951 ± 27</td><td>0.834 ± 0.015</td></tr><tr><td>C</td><td><strong>144,290 ± 12</strong></td><td><strong>62 ± 12</strong></td><td><strong>58 ± 10</strong></td><td><strong>982 ± 10</strong></td><td><strong>0.943 ± 0.008</strong></td></tr><tr><td rowspan="12">ILL12</td><td rowspan="3">Random Forest</td><td>A</td><td>144,105 ± 3</td><td>15 ± 3</td><td>689 ± 4</td><td>631 ± 4</td><td>0.642 ± 0.003</td></tr><tr><td>B</td><td>144,032 ± 2</td><td>88 ± 2</td><td>316 ± 3</td><td>1,004 ± 3</td><td>0.833 ± 0.002</td></tr><tr><td>C</td><td>144,036 ± 0</td><td>84 ± 0</td><td>328 ± 3</td><td>992 ± 3</td><td>0.828 ± 0.001</td></tr><tr><td rowspan="3">XGBoost</td><td>A</td><td>143,814 ± 0</td><td>306 ± 0</td><td>424 ± 0</td><td>896 ± 0</td><td>0.711 ± 0.000</td></tr><tr><td>B</td><td>143,994 ± 0</td><td>126 ± 0</td><td>252 ± 0</td><td>1,068 ± 0</td><td>0.850 ± 0.000</td></tr><tr><td>C</td><td>143,996 ± 0</td><td>124 ± 0</td><td>244 ± 0</td><td>1,076 ± 0</td><td>0.854 ± 0.000</td></tr><tr><td rowspan="3">LSTM</td><td>A</td><td>143,962 ± 51</td><td>110 ± 51</td><td>190 ± 50</td><td>1,130 ± 50</td><td>0.883 ± 0.008</td></tr><tr><td>B</td><td>143,933 ± 35</td><td>139 ± 35</td><td>73 ± 29</td><td>1,247 ± 29</td><td>0.922 ± 0.002</td></tr><tr><td>C</td><td><strong>143,975 ± 12</strong></td><td><strong>97 ± 12</strong></td><td><strong>71 ± 11</strong></td><td><strong>1,249 ± 11</strong></td><td><strong>0.937 ± 0.003</strong></td></tr><tr><td rowspan="12">ILL13</td><td rowspan="3">Random Forest</td><td>A</td><td>144,065 ± 1</td><td>40 ± 1</td><td>461 ± 3</td><td>874 ± 3</td><td>0.777 ± 0.002</td></tr><tr><td>B</td><td>144,055 ± 2</td><td>50 ± 2</td><td>262 ± 5</td><td>1,073 ± 5</td><td>0.873 ± 0.002</td></tr><tr><td>C</td><td>144,053 ± 0</td><td>52 ± 0</td><td>249 ± 3</td><td>1,086 ± 3</td><td>0.878 ± 0.001</td></tr><tr><td rowspan="3">XGBoost</td><td>A</td><td>143,934 ± 0</td><td>171 ± 0</td><td>357 ± 0</td><td>978 ± 0</td><td>0.787 ± 0.000</td></tr><tr><td>B</td><td>143,995 ± 0</td><td>110 ± 0</td><td>224 ± 0</td><td>1,111 ± 0</td><td>0.869 ± 0.000</td></tr><tr><td>C</td><td>143,990 ± 0</td><td>115 ± 0</td><td>219 ± 0</td><td>1,116 ± 0</td><td>0.870 ± 0.000</td></tr><tr><td rowspan="3">LSTM</td><td>A</td><td><strong>143,992 ± 21</strong></td><td><strong>65 ± 21</strong></td><td><strong>103 ± 23</strong></td><td><strong>1,232 ± 23</strong></td><td><strong>0.936 ± 0.005</strong></td></tr><tr><td>B</td><td>143,822 ± 48</td><td>235 ± 48</td><td>87 ± 22</td><td>1,248 ± 22</td><td>0.886 ± 0.011</td></tr><tr><td>C</td><td>143,920 ± 23</td><td>137 ± 23</td><td>93 ± 24</td><td>1,242 ± 24</td><td>0.915 ± 0.005</td></tr></tbody></table>

Note. The values before and after ± represent the mean and standard deviation across five repetitions. The bold values indicate the maximum value model-feature combinations for each station. The feature types are identified in Figure 3 with their full names listed in Text S1. A detailed comparison of F1 scores for different model and feature combinations is provided in Table S4 in Supporting Information S1.

Only Random Forest exhibited notable 95% confidence interval ranges (±0.427 to ±0.518) for specific features within the Benford's Law set: first digit frequency 3 (ID 2), first digit frequency 6 (ID 5), and goodness of fit (ID 10). Consequently, we present the mean feature weights in Figure 3 and the mean feature weights with 95% confidence intervals in Figure S3 in Supporting Information S1.

Compared to the Random Forest, the XGBoost model exhibited greater variation in feature weights across all Type C features, while showing less variance for individual features (Figure 3 and Figure S3 in Supporting Information S1). XGBoost model only outputs less than four features with a normalized weight greater than 0.2 for the three selected stations. Within the features related to Benford's Law (IDs 0–10), Random Forest assigned greater weights than XGBoost, but also exhibited greater variance, with maximum 95% confidence interval ranges from ±0.427 to ±0.518 (Figure S3 in Supporting Information S1). For the waveform features (IDs 11–35), both seismic stations and ensemble machine learning models indicated that the binary debris flow classification task relies on IDs 22–26 features (energy of signals filtered in some frequency bands, denotes as $ES_{f_1-f_2}$). However, the most important frequency band differed slightly among stations and models. For example, for both Random Forest and XGBoost models, the most important features or frequency bands were $ES_{5-15}$ (ID 23), $ES_{15-25}$ (ID 24), $ES_{5-15}$ (ID 23) for
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Comparison of model-feature combination performance based on McNemar's test. Panels (a) to (c) present the mean results from five repetitions for the ILL18, ILL12, and ILL13 stations. In each subplot, the upper-right triangular heatmap represents the results of hypothesis tests ($H_0$: There is no significant difference between the two model-feature combinations) conducted using McNemar's test. Rejection of $H_0$ denotes a p-value $\le 0.05$. The lower-left triangular heatmap shows the values of $b-c$ in the contingency table (refer to Table S3 in Supporting Information S1 for the definition of $b$ and $c$. Positive values ($b-c>0$) indicate that Combination 1 outperforms Combination 2, while negative values ($b-c<0$) denote that Combination 1 underperforms Combination 2.</figcaption></figure>

ILL18, ILL12, and ILL13, respectively. In addition, the ID 35 feature (interquartile range) was also important for Random Forest models relative to other waveform features.

The features in the spectra set (IDs 36–52), spectrogram set (IDs 53–69), and network set (IDs 70–79) provide limited assistance (Figure 3). Between ID 36 and ID 52, the spectra feature only showed contributions only from those related to the discrete Fourier transform (DFT) (e.g., ID 37, Max of the DFT) and energy between different Nyquist frequencies (NyF) (e.g., ID 46 and 47, energy between 0 and 1/4, 1/4 and 1/2 NyF), contributing to the Random Forest model. Similarly, the spectrogram features (IDs 53–69) include only ID 55 (mean ratio between the max and the mean of all discrete Fourier transform DFT), ID 56 (mean ratio between the max and the median of all discrete Fourier transform DFTs), ID 57 (number of peaks in the curve showing the temporal evolution of the max DFTs), ID 58 (number of peaks in the curve showing the temporal evolution of the mean DFTs), ID 60 (ratio of ID 57 to ID 58) were limited helpful for Random Forest model at ILL12 station and ILL13 station (Figure 3b). Our results showed the seismic network based metrics (IDs 70–79) features were not informative for the single-station-based binary debris flow classification task (Figures 3a–3c).

### 4.3. Debris Flow Warning via Multiple Stations

Machine learning models trained at a single station can issue timely alarms via the seismic network with three stations, using our warning strategy (Figures 4 and 5). The optimal parameters of the warning strategy for different model-feature combinations that achieve the maximum warning time for all validation events are presented in Table 4. We observed that varying attention window sizes and global average probability thresholds influenced the occurrence of false warnings (Figures 4 and 10). The models trained with Type C features generally outperformed those trained with Type A features. While Type B features are similar to Type C features, and the optimal parameter settings for the warning strategy differ slightly. For example, with an attention window size ($l$) of 13 min and a global average probability ($Pr_g$) of 0.1, the LSTM model trained with Type C features achieved the highest performance. It provided a minimum warning time of 31.9 min and a mean warning time of 121.6 min for the twelve validation events (Table 4), compared to the debris flow front first reaching CD29. The detailed warning time for each events are displayed in Figure 4.

The warning time generated by our model was generally shorter than the benchmark Pre-CD1 warning $W_{t1}$ but longer than the benchmark Post-CD1 warning $W_{t2}$ (Figures 4 and 5). For example, we presented the results from Type C features and choose the attention window size and global averaged probability as Table 4. The Random Forest model trained with Type C features (Figure 5b) issued the first warning at 04:38:00 UTC+0 for the 2020-06-29 debris flow (E8), providing an additional 71 min of warning time before the flow reached CD29 at
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Normalized seismic feature weights from Random Forest and XGBoost models, calculated based on the mean feature values from five repetitions. Panels (a) to (c) are the results from seismic stations ILL18, ILL12, and ILL13. Background shading is utilized to distinguish between the Benford's Law set (IDs 0–10), the waveform set (IDs 11–35), the spectra set (IDs 36–52), the spectrogram set (IDs 53–69), and the seismic network based metrics set (IDs 70–79). The features in Type A index IDs 0–10, the features in Type B index IDs 11–79, the features in Type C index IDs 0–79. A detailed list of feature names and weights is provided in Text S1 in Supporting Information S1.</figcaption></figure>

05:49:13. This was approximately 4.5 min later than Pre-CD1 warning (04:33:29) and 32 min earlier than Post-CD1 warning (05:10:09), respectively. XGBoost issued warnings at 04:42:00 (Figure 5c). The LSTM model issued the first warning at 04:36:00, the earliest among the three models. It demonstrated greater robustness to transient noise or disturbances, owing to its stable prediction of debris flow probability, particularly for debris flows with multiple surges, as observed in events E7a and E7b (Figure 5d and Figure S2 in Supporting Information S1).

# 5. Discussion

## 5.1. Learning From Imbalanced Data for Binary Debris Flow Classification

In real scenarios, the volume of seismic data generated by debris flows (labeled as DF class) is vastly outweighed by background noise (labeled as Non-DF class). For example, in our data set collected from the ILL12 station between 2017 and 2019, the ratio of Non-DF to DF labels is approximately 230:1 ≈ 1:0.004 (Table 1). This data imbalance may present challenges when training a machine learning model for binary debris flow classification. Because the model can become biased toward the majority class, Non-DF label, due to the disproportionate influence of the loss function during training. To address the issue of imbalanced data, two widely adopted strategies operate at the data level (e.g., data pre-processing) and algorithm level (e.g., cost-sensitive learning) (Haixiang et al., 2017; Krawczyk, 2016; Werner de Vargas et al., 2023). One approach at the data level is to balance the data set using techniques such as oversampling the minority class or undersampling the majority class. Alternatively, at the algorithm level, cost-sensitive learning methods can be used, including customizing the loss
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Increased warning times were issued by different model-feature combinations. Panels (a) to (c) show the results of Random Forest, XGBoost, and Long Short-Term Memory. In subplot (a), the attention window size (*l*) and global average probability (*Pr*<sub>*g*</sub>) are set to 11 and 0.2 for the combination of Random Forest and Type A; the failed warned events E5 are marked as gray rectangles. For other model-feature combinations, *l* and *Pr*<sub>*g*</sub> are displayed in Table 4. Pre-CD1 (orange horizontal line) and Post-CD1 (green horizontal line) indicate the increased time warning time based on the first Pre-CD1 and Post-CD1 timestamps in the benchmark results (Table 2). The failed warnings of benchmark results for Pre-CD1 in event E5 and Pre-CD1 in event E7a are marked with gray horizontal lines.</figcaption></figure>

function to assign higher penalties for misclassifying the minority class and adjusting hyperparameters (e.g., class weights) to emphasize the minority class. Furthermore, the Non-DF label may encompass a diverse range of events, including other mass movements, fluvial processes, human activities, and near-field unknown events. Incorporating all Non-DF labels into the model during the training process may provide valuable human prior knowledge, enhancing the model's generalizability and robustness.

Using the ILL12 station and Type C as an example, we discuss (a) the impact of imbalanced data on the training process through undersampling of the 2017–2019 training data, and (b) the effect of assigning different weights to the minority DF class. Following the training and validation workflow described in Section 3.4, we first reserved all DF label data from 2017 to 2019. Then, using fixed class weights of 0.9 and 0.1 for the DF and Non-DF labels, respectively, we created data sets with varying Non-DF to DF class ratios by randomly undersampling the Non-DF label data (Text S5 in Supporting Information S1). Next, incorporating all Non-DF and DF labels from the 2017–2019 data, we assign different weights to the DF class. To reduce the uncertainty introduced by the random selection process, we repeat the training and validation process five times and present the results in Figure 6.

For the undersampling method, the Random Forest and XGBoost models show no variation in training performance with different ratios of Non-DF to DF classes, while the training performance of the LSTM model tends to decrease as the proportion of Non-DF samples increases (Figure 6a). The testing results consistently show that the performance of all three models improves as the ratio increases (Figure 6b). At the same time, the precision and recall of the three models exhibit similar trends, increasing and then decreasing, respectively, with the ratio of the Non-DF class to the DF class (Figure S6b in Supporting Information S1). This indicates that as more diverse data
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Waveform recorded at three seismic stations and predicted 2020-06-29 debris flow (E8) probability from machine learning models trained using Type C features: (a) Waveform from upstream station ILL18, midstream ILL12, and downstream ILL13. Panels (b) to (d) are debris flow probability and final issued warning from Random Forest model (b), XGBoost model (c), and Long Short-Term Memory model (d). The gray and light red backgrounds in (b) to (d) show the final warning time period. The attention window size (*l*) and global average probability (*Pr*<sub>g</sub>) for issuing the final warning are referred to in Table 4. The vertical dashed lines present the time stamps of Pre-CD1 warning *W*<sub>f1</sub> and Post-CD1 warning *W*<sub>f2</sub> from benchmark results (Table 2). The vertical green solid line indicates the time stamp 2020-06-29 05:49:13 when the debris flow front first reached station CD29.</figcaption></figure>

are used for training, the models tend to recognize fewer false positives (misclassifying Non-DF as DF) at the cost of an increased number of false negatives (mainly misclassifying DF in the early and late stages as Non-DF). The intersection points of precision and recall for all three models occur at approximately 50, 100, and 200, suggesting that XGBoost and LSTM require more training data to achieve optimal performance.

For the class weight method, the Random Forest model demonstrates insensitivity to variations in class weights and may exhibit overfitting (Figure 6c), while the XGBoost model experiences a decrease in both training and testing performance when the class weight is excessively high (e.g., when the DF class weight is set to 0.97 in this study). Our results also show that the LSTM model does not exhibit significant trend with respect to class weights (Figure 6d), and the Type A and Type B demonstrate the same trend as Type C (Figures S7 and S8 in Supporting Information S1). The same trend was observed in Figure S6d in Supporting Information S1, where the XGBoost model exhibited sensitivity to DF class weight, whereas the Random Forest and LSTM models did not, as indicated by the precision and recall metrics. To enhance model robustness and performance in real-world scenarios, we propose utilizing all available data for training. The Non-DF class, which may encompass events such as earthquakes, rockfalls, vehicle movements, human activities, and background noise, can exhibit temporal similarities with debris flow signatures. Exposing the model to this diverse data set is crucial for accurate
<table><caption>**Table 4**
Optimal Parameters of the Warning Strategy for Different Model-Feature Combinations</caption><thead><tr><th rowspan="2">Model Type</th><th rowspan="2"></th><th colspan="3">Feature type</th></tr><tr><th>A</th><th>B</th><th>C</th></tr></thead><tbody><tr><td></td><td>RF</td><td>N/A</td><td>l = 11, Pr<sub>g</sub> = 0.2,<br>mean(Δ<sub>t</sub>) = 107.3,<br>min(Δ<sub>t</sub>) = 29.9</td><td>l = 11, Pr<sub>g</sub> = 0.2,<br>mean(Δ<sub>t</sub>) = 107.3,<br>min(Δ<sub>t</sub>) = 28.9</td></tr><tr><td></td><td>XGBoost</td><td>l = 7, Pr<sub>g</sub> = 0.3,<br>mean(Δ<sub>t</sub>) = 104.5,<br>min(Δ<sub>t</sub>) = -3.3</td><td>l = 14 (or 2), Pr<sub>g</sub> = 0.2 (or 0.4),<br>mean(Δ<sub>t</sub>) = 107.3,<br>min(Δ<sub>t</sub>) = 29.9</td><td>l = 15, Pr<sub>g</sub> = 0.2,<br>mean(Δ<sub>t</sub>) = 106.8,<br>min(Δ<sub>t</sub>) = 29.9</td></tr><tr><td></td><td>LSTM</td><td>l = 13, Pr<sub>g</sub> = 0.2,<br>mean(Δ<sub>t</sub>) = 118.7,<br>min(Δ<sub>t</sub>) = 28.9</td><td>l = 5, Pr<sub>g</sub> = 0.2,<br>mean(Δ<sub>t</sub>) = 121.3,<br>min(Δ<sub>t</sub>) = 29.9</td><td>l = 13, Pr<sub>g</sub> = 0.1,<br>mean(Δ<sub>t</sub>) = 121.6,<br>min(Δ<sub>t</sub>) = 31.9</td></tr></tbody></table>

Note. N/A indicates that no combination of attention window size (l) and global average probability (Pr<sub>g</sub>) can ensure warnings for all events without false warnings. The unit for increased warning time Δ<sub>t</sub> is minute. The total tested time is from 2020-05-29T01:00:00 to 2020-09-06T23:00:00, which have 145,320 one-minute non-overlapping moving windows.

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Statistical analysis of the impact of imbalanced data and class weights on model performance, using the ILL12 station and Type C feature as an example. Panels (a) and (b) display the F1 scores from the training results with varying ratios of Non-DF (non-debris flow) to DF (debris flow) classes in the 2017–2019 training data and the validation results from the trained model using the entire 2020 data set. The DF class weight was fixed at 0.9. Panels (c) and (d) present the F1 scores from the training and validation results with varying DF class weights. The entire 2017–2019 training data set and the full 2020 testing data set were used. Different colors represent the three models, and the shaded regions indicate the 95% confidence intervals based on five independent repetitions. The Non-DF class weight equals one minus the DF class weight.</figcaption></figure>
discrimination. Regarding the weighting of the minority class within this data set, we recommend empirical experimentation to determine optimal values tailored to the specific task and data set characteristics.

## 5.2. Seismic Feature and Debris Flow Process

Machine learning models are designed to learn underlying feature patterns and relationships in data in order to make accurate predictions. However, unlike models based on physical laws, it is challenging for these machine learning models to explain or link input training data to physical processes. In this work, we utilize Random Forest and XGBoost models to provide explainability for input seismic features, guiding the design of warning systems and enhancing our ability to transfer the model to different sites. For instance, our results highlight that the first digit frequency is helpful for the Random Forest model (IDs 0–8 in Figure 3 and Text S1 in Supporting Information S1), while the seismic feature named energy filtered between frequencies $f_1$ and $f_2$ Hz ($ES_{f_1-f_2}$, IDs 22 to 26 in Figure 3 and Text S1 in Supporting Information S1) are essential both for Random Forest and XGBoost model to perform the binary debris flow classification task. For predicting rockfall mass and velocity, Hibert et al. (2024) also demonstrated that seismic energy in a specific frequency band is the most important feature in the Random Forest model. For the Illgraben catchment, Belli et al. (2022) showed that the seismic frequency at station ILL13 remains stable for the debris flow between 2017 and 2019, with values ranging from 6.8 to 7.2 Hz. In this work, we also observed that the first discriminative seismic feature for both Random Forest and XGBoost are $ES_{5-15}$ at station ILL13. We used Equation 5 to calculate $ES_{f_1-f_2}$ as (Provost et al., 2017):

$$ES_{f_1-f_2} = \int_{0}^{T} S_{f_1-f_2}(t) dt, \qquad (5)$$

where T is the length of the time window, $S_{f_1-f_2}(t)$ is the envelope of seismic signal filtered between frequencies $f_1$ and $f_2$. While the closer-channel station ILL12 shows that $ES_{5-25}$ is more important than $ES_{5-15}$. This can be explained by the attenuation of seismic surface waves during propagation, as described by Equation 6 (Aki & Richards, 2002):

$$A(d) \propto \frac{A_0 e^{-\frac{d}{Q}}}{\sqrt{d}}, \qquad (6)$$

where $A_0$ is the amplitude at the source location, d is the source-receiver distance, f is the frequency of interest, Q is the ground quality factor, and v is the seismic wave velocity. If we assume that Q and v are constant for all debris flows, $ES_{f_1-f_2}$ is directly proportional to the source-receiver distance d and inversely proportional to the frequency of interest. The closest distances between the seismic stations and debris flow channel in this work are approximately $d_{ILL18} \approx 380$ m, $d_{ILL12} \approx 90$ m, and $d_{ILL13} \approx 530$ m, respectively. This explains why the ILL18 station shows that both Random Forest and XGBoost rely on $ES_{5-15}$ and the ILL12 station is more dependent on high-frequency signals $ES_{5-25}$. Note that the peak seismic frequency is not constant, but rather decreases as the source-receiver distance increases. For example, a peak frequency of approximately 55 Hz was observed at 11 m (frequency averaged from four debris flow events; data source: Figure 10e in Y. Yang et al. (2024)), while a peak frequency of approximately 33 Hz was observed at 20 m (frequency from the 2021-07-16 debris flow event; data source: Figure 9a Porter et al. (2023)). However, while it is qualitatively recognized that debris flow footprints recorded by infrasound or seismic data resemble the shape of a cigar (Belli et al., 2022; Farin et al., 2018; Marchetti et al., 2019), it remains unclear to what extent the seismic signatures of debris flows at our study site are representative of other catchments. In order to train the machine learning model for other catchments, it is necessary to delineate the frequency band for feature extraction based on the source-receiver distance and peak seismic frequency obtained through spectra analysis for the event of interest.

For the feature weights output by the ensemble models, it is critical to emphasize that these weights are dependent on the model's training configuration (e.g., window size used for feature calculation and training labels) and hyperparameters (e.g., debris flow class weight and number of trees). We document the key methodological distinctions between our training procedure and Chmiel et al. (2021) in Text S3 in Supporting Information S1. According to Chmiel et al. (2021), the three most important features among Type B to train Random Forest for Pre-CD1 (debris flow before CD1 or early debris flow), Post-CD1 (debris flow after CD1 or late debris flow), and
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Effect of input stations used on seismic network features. (a) Normalized ratio between the maximum root mean square (RMS) and minimum RMS. (b) Normalized maximum coherence (cross-correlation) between station pairs. The black and red line denotes the seismic features extracted from three stations and six stations. The 2019-06-21 debris flow case with a volume of about 83,000 m³ crossed check dams CD1 and CD29 at UTC+0 2019-06-21 19:34:42 (vertical blue dashed line), 2019-06-21 20:45:44 (vertical blue solid line), respectively (Zhang et al., 2021).</figcaption></figure>

noise are from the seismic network based metrics set. These features include the ratio between the maximum root mean square (RMS) of the seismic signal (RMS of seismic signals, abbreviated as RMS) and the minimum RMS in the network (ID 72 in Figure 3), the station number with the maximum RMS (ID 70), and the maximum coherence (normalized cross-correlation) between station pairs (ID 75). Furthermore, in a seismic network with four stations located at the apex and centers of equilateral triangles, Provost et al. (2017) computed Type B features by treating the events of interest with varying duration as a single segment, unlike in this work, where one-minute non-overlap windows were used. They demonstrated that the most discriminant feature to train Random Forest to detect rockfall events, earthquakes, and anthropogenic noise is the ratio between the maximum and minimum amplitude recorded in this network. If the model's classification performance is highly dependent on network features or the distribution of the seismic network, it poses a risk to the reproducibility of the trained model.

We briefly discuss why seismic features in the network set do not significantly contribute to our binary classification task and present their correlation to the physical processes of debris flows. The RMS for given time windows indicates the magnitude of a varying quantity or dispersion of the seismic observed amplitude $[A(d)_1, A(d)_2, ..., A(d)_n]$ at time $[1, 2, ..., n]$. Although the RMS is influenced by the local background noise level, using the same data for training and testing as Chmiel et al. (2021), we could approximate the distance between the excitation source and the seismic station based on the maximum and minimum RMS (ID 70 and 72). During the background noise or Non-debris flow period, minimum RMS should be constant over time (Figure 7 and Figure S4 in Supporting Information S1), and maximum RMS is expected to be random. During the debris-flow period, if we disregard non-stationary noise sources (natural or anthropogenic, which also affect the entire seismic network but with a low probability), the seismic amplitudes generated by the debris flow are primarily dominated by the flow front (Farin et al., 2019). Therefore, a moving distributed source that travels as the debris flows can be simplified as a point source only considering the front (Walter et al., 2017). Assuming *Q* and *v* in Equation 6 remain constant during all debris-flow periods, the RMS observed in a given window will be proportional to amplitude as RMS ∝ *A*(*d*). For example, when the debris flow front is near CD29 at some point, the minimum RMS values at closer stations (ILL18, ILL12, and ILL13) will theoretically be larger than those at distant stations (ILL16, ILL17, and ILL14). In this case, using only the information from ILL18, ILL12, and ILL13 and excluding ILL16, ILL17, and ILL14 stations will maintain the maximum RMS value in the seismic network, but the ratio of maximum RMS to minimum RMS (ID 72) will decrease. Additionally, the shape of RMS could also change over time (Figure 7a). Therefore, the feature ID 72 may greatly contribute to the debris flow classification task within a dense and decentralized seismic network setup. However, there is a trade-off between warning precision and the
complexity of the seismic network, which includes factors such as the number of seismic stations and their distribution.

In practical application, the previous approach, as exemplified by Chmiel et al. (2021) and Provost et al. (2017), limited the transferability of the trained model due to the challenge of obtaining data from dense seismic stations with the same or similar spatial distribution as the Illgraben catchment. Du et al. (2023) employed field measurement data to extract dimensionless characteristics of debris flows (e.g., Einstein number and Bagnold number) and provided evidence that sediment concentration and dimensionless discharge in the Illgraben catchment fall in the range of values observed in other debris flow catchments (e.g., Chalk Cliffs, and Jiangjia). We recommended installing geophones or seismometers in a safe location near the channel, as was done in ILL12, and refer to our trained models for debris flow classification and warning. Nevertheless, the universal seismic features to detect debris flows and optimal strategies for issuing warnings still need to be explored.

## 5.3. Impact of Seismic Feature on Machine Learning Model Performance

In situations where there is no large amount of seismic data available to train machine learning models, as in our work, extracting more features improves the performance of machine learning models for the binary debris flow classification task and the debris flow alarm task (Khalid et al., 2014). However, irrelevant or unimportant features in machine learning training may lead to model overfitting and hinder model generalization (Hall & Holmes, 2003; Kotsiantis, 2011; Solorio-Fernández et al., 2020). For example, when using the LSTM model to predict time-to-failure trends during triaxial stick-slip experiments, Karimpouli et al. (2023) found that the statistical coefficient of determination ($R^2$) was 70.7%, 56.3%, and 45.6% using 47 acoustic emission features, seven most important features, or 40 non-most important features (removing the most important features), respectively. This indicates that the performance of machine learning models, such as LSTM, is not consistently correlated with the number of input features.

Here, we calculated the Spearman correlation coefficient $\rho$ to assess input seismic feature correlation and explore whether Type C affects by redundant input information (Text S4 in Supporting Information S1). $\rho$ indicates the strength and direction of the relationship between two selected features. Larger absolute values indicate a stronger correlation (Hauke & Kossowski, 2011; Myers & Sirois, 2004). The seismic features from 2017 to 2019 training data were scaled by Equation 1. Our results show that Benford's law (ID 0–10) and spectra (ID 36–52) features are highly correlated within each set, and Benford's law features also correlate with some waveform (ID 11–35) features (Figure 8). For example, interquartile range (ID 35) is highly positively correlated with $ES_{15-25}$ (ID23), and energy between 1/4 and 1/2 Nyquist frequency (ID 47) is negatively correlated with mean ratio between the max and the median of all DFT (ID 56) (Figures 8b and 8c). Therefore, we argue that both the existing approach proposed by Chmiel et al. (2021) and Provost et al. (2017) with Type B features and our work with Type C features to train the debris flow detector may be affected by redundant input information.

To reduce the input information redundancy during machine learning model training and simplify the computational complexity of the model, we explore the model's performance as a function of the number of input seismic features. Following the training and validation workflow described in Section 3.4, we reduced the input seismic features in Type C, using three strategies. Considering the feature weights derived for each model and station (as shown in Figure 3), the first strategy iteratively removed the least important features, while the second iteratively removed the most important features. The third strategy involved random feature removal, disregarding feature weights. To ensure result reproducibility, the first two strategies were repeated five times, and the third strategy was repeated 100 times. Due to graphics processing unit computational resource limitations, we restricted our LSTM model experiments to the ILL12 station, varying the number of randomly selected features across the following set: [75, 60, 40, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]. Here, we present the validation results for the single-station-based binary debris flow classification task, and the training results could be found in Figure S5 in Supporting Information S1.

We observed that removing approximately 60 features, whether removed from the least important, most important, or randomly selected features, resulted in consistent validation F1 for both the XGBoost and LSTM models across all three stations (Figure 9). However, the Random Forest model exhibited greater sensitivity to the removal of the most important features, showing an earlier decline in F1 score (Figure 9a). This behavior can be attributed to the underlying algorithm structures. The Random Forest model constructs multiple decision trees
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Feature correlations from the 2017–2019 training seismic data: (a) Spearman correlation coefficient $ρ$. Larger positive values of $ρ$ denote more positive monotonic (two features contain similar information), and vice versa. Benford's law (IDs 0–10), waveform (IDs 11–35), spectra (IDs 36–52), spectrogram (IDs 53–69), and network (IDs 70–79) features. The feature names listed in Text S1 in Supporting Information S1. Panels (b) to (c) are examples of positive and negative correlation features. ID 23, ID 35, ID 47, and ID 56 are $ES_{5-15}$, interquartile range, energy between 1/4 and 1/2 Nyquist frequency, mean ratio between the max and the median of all discrete Fourier transform, respectively.</figcaption></figure>

independently, relying on random feature subsets for splitting (Breiman, 2001). Consequently, reducing the number of available features, particularly the most important ones, diminishes tree diversity, potentially leading to a rapid decrease in model performance. In contrast, XGBoost builds trees sequentially, with each tree correcting errors from its predecessors (Chen & Guestrin, 2016). This approach enables the model to focus more on highly important features, as reflected in the feature importance map (Figure 3). The sequential structure enables XGBoost to prioritize highly important features and adapt more effectively to feature removal. Similarly, LSTM, with its deeper neural network architecture, excels at capturing sequential patterns, making it less dependent on the number of features. Theoretically, the number of seismic features used is not linearly related to machine learning model performance. However, our results suggest that the majority of seismic features introduced by Chmiel et al. (2021) and Provost et al. (2017) do not contribute to training the machine learning model. Therefore, we argue that machine learning models are less dependent on the number and even type of input seismic features for the single-station-based binary debris flow classification task. It is advisable to consider integrating seismic data from different stations in order to enhance the accuracy of warnings, rather than focusing solely on the quantity of input seismic features.

5.4. Machine Learning Model Evaluation and Warning Strategy

Our results show that machine learning models can perform the binary debris flow classification task well at the catchment scale with different types and numbers of feature, as illustrated in Table 3, Figures 4 and 9. Focusing on a single station, the type of machine learning model reveals only minor performance differences for the single-station-based binary classification task. Hyperparameter tuning for those models based on data from the Illgraben catchment may further improve the model classification performance (Feurer & Hutter, 2019; L. Yang & Shami, 2020). Currently, our trained models are intended to function as debris flow classifier in various catchments where data are insufficient to train the model, rather than being optimized specifically for Illgraben. Unlike the classifier developed by Chmiel et al. (2021), our model can operate independently of the seismic network
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Performance of the machine learning model as a function of the number of input seismic features. Panels (a) to (c) display the validation F1 scores for different seismic stations. The least-to-most feature removal approach involved ranking the Type C features by their importance weights (as shown in Figure 3) for each model and station in descending order, followed by sequentially removing the least important features one by one. The most-to-least feature removal approach followed the reverse order. In the random feature removal approach, features were removed randomly. The training F1 scores are displayed in Figure S5 in Supporting Information S1.</figcaption></figure>

feature set. It can be simplified to utilize just 10 or 20 key features (Figure 9), enhancing its usability even when working with data from a single station.

For the multi-station-based warning task, our warning strategy requires different combinations of attention window size (*l*) and global average probability (*Pr*<sub>*g*</sub>) to balance the trade-off between the number of failed warning event and false warnings (Figure 10, Figures S9 and S10 in Supporting Information S1). Overall, the LSTM model provides longer warning times compared to the Random Forest and XGBoost models, even when using Type A features (Table 4). This advantage is due to the LSTM's ability to capture time-series information more effectively than the ensemble models. As expected, lower values of *Pr*<sub>*g*</sub> result in more events being warned, but also lead to a higher number of false warnings. Conversely, increasing *l* reduces false warnings but also decreases the overall lead time of the warnings. False warnings are usually due to short-duration processes not observed by all stations in the network, which may be related to other mass movements or local anthropogenic process. In Table 4, for the period between 2020-05-29T01:00:00 and 2020-09-06T23:00:00, we present only the warning strategy that provides the maximum increased warning time. Because multiple combinations of attention window size (*l*) and global average probability (*Pr*<sub>*g*</sub>) can guarantee no false or missed warnings (Figure 10), these parameters should be determined based on local stakeholders and the acceptable levels of risk. In regions prone to rapid-onset, high-volume debris flows with significant catastrophic potential, particularly those encompassing vulnerable populations in high-density areas (e.g., schools and densely populated residential zones), warning systems may prioritize minimizing missed warning events over false alarms (shorter *l* and lower *Pr*<sub>*g*</sub>). In contrast,
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Multi-station-based warning performance under different attention window size ($l$) and global average probability ($Pr_g$). Row (a) to (c) display the results for models trained with Type C features using Random Forest, XGBoost, and Long Short-Term Memory, respectively. Combinations of $l$ and $Pr_g$ that result in no failed warnings or false warnings are marked as 0 in the first and second columns. The models trained with Type A and Type B features are displayed in Figures S9 and S10 in Supporting Information S1.</figcaption></figure>

in settings where debris flows primarily threaten high-value public infrastructure (e.g., critical transportation networks, industrial complexes, or tourism-dependent zones), systems may prioritize minimizing false alarms over missed warned events (longer $l$ and higher $Pr_g$). Additionally, our warning strategy currently relies on only three stations, disregards the spatial distribution of the seismic network, and does not consider the temporal sequence of detections across different stations. Future research should further refine the warning strategy by considering the seismic network distribution and the physical properties of debris flows. For example, incorporate the local digital elevation model or the relative positional relationships between seismic stations into the training process of the machine learning models.

## 5.5. Limitations and Future Directions

Supervised machine learning, which relies on labeled data and features, may reduce the potential for model generalization (Mousavi & Beroza, 2023). The transferability of our models, trained on data from the Illgraben catchment, to other locations remains to be tested. For single-station setups, focusing on the most influential features (e.g., $ES_{5-15}$) may enhance the likelihood of successful application to other sites without the need for retraining. Moreover, incorporating seismic data from various catchments could further optimize model performance by accounting for the variability in seismic signals generated by debris flows. Our proposed workflow enables the exploration of multiple data sets from different catchments, potentially leading to the establishment of
a repository for debris flow seismic catalogs. This could ultimately support the training of a more generalized machine learning model for debris flow classification and warning. However, our pre-defined warning strategy still faces challenges in fully utilizing predicted debris flow probabilities across multiple stations and in handling sensor malfunctions automatically.

## 6. Conclusion

Our study highlights the complexities and opportunities in utilizing machine learning for debris flow early warning. By modularizing seismic-driven debris flow early warning systems into single-station-based classification and multi-station-based warning components, our approach enables the implementation of early warning with just one or two seismic stations, even in the absence of a dense seismic network. Furthermore, although features such as seismic energy in specific frequency bands (e.g., 5–15 Hz and 25–25 Hz) are identified as more important based on ensemble models, our work also demonstrates that the number and even the types of seismic features are not critical for training a reliable binary classifier for debris flow. We also found that the LSTM model, which focuses on identifying data patterns, is more effective than ensemble models in consistently predicting for multi-station-based early warning. To implement the multi-station-based warning task, machine learning models should be used to capture time-series information (or footprints of events of interest) instead of stacking seismic features. We anticipate that our methodology can be applied to gravity-driven excitation sources moving along the channel (e.g., glacial lake outburst floods and lahars) without altering the structure of machine learning models. However, the warning strategy and trained model generalization require further updates in the next step.

## Data Availability Statement

All seismic data analyzed in this study are available at Zurich (2012). The physical parameters of debris flow from 2017 to 2020 are available at Chmiel et al. (2021); McArdell and Hirschberg (2020); McArdell et al. (2023). The trained machine learning models and source code are publicly available on Zenodo Q. Zhou (2025) and GitHub (https://github.com/Qi-Zhou-Geo/Flow-Alert.git).

## Acknowledgments

This work utilized high-performance computing resources made possible by funding from the Ministry of Science, Research and Culture of the State of Brandenburg (MWFK) and is operated by the IT Services and Operations unit of the GFZ Helmholtz Centre for Geosciences. The authors thank the Swiss Seismological Service for their assistance with installing seismometers and archiving seismic data. This study is funded by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Open Fund (SKLGP2023K003), and the National Natural Science Foundation of China (42120104002). The authors thank Danu Caus, Harsh Grover, and Adeniyi Mosaku from the DKRZ German Climate Computing Center for their discussion and feedback on the Long-Short Memory model. The authors also thank Dr. Erik Ngai Ham Chan from the Alfred Wegener Institute's research center in Potsdam for his discussion on the machine learning model, Kshitij Kar from the University of Potsdam for his help with the data processing. The authors thank the editors and anonymous reviewers for their valuable comments and for encouraging us to incorporate statistical tests. Open Access funding enabled and organized by Projekt DEAL.

## References

Abdalzaher, M. S., Moustafa, S. S., Abd-Elnaby, M., & Elwekeil, M. (2021). Comparative performance assessments of machine-learning methods for artificial seismic sources discrimination. *IEEE Access, 9*, 65524-65535. https://doi.org/10.1109/access.2021.3076119

Abdalzaher, M. S., Moustafa, S. S., Hafiez, H. A., & Ahmed, W. F. (2022). An optimized learning model augment analyst decisions for seismic source discrimination. *IEEE Transactions on Geoscience and Remote Sensing, 60*, 1-12. https://doi.org/10.1109/tgrs.2022.3208097

Abraham, K., Gassiat, E., & Naulet, Z. (2022). Fundamental limits for learning hidden Markov model parameters. *IEEE Transactions on Information Theory, 69*(3), 1777-1794. https://doi.org/10.1109/tit.2022.3213429

Aki, K., & Richards, P. (2002). *Quantitative seismology*. University Science Books. Retrieved from https://books.google.de/books?id=sRhawFG5_EcC

Arattano, M., & Marchi, L. (2008). Systems and sensors for debris-flow monitoring and warning. *Sensors, 8*(4), 2436-2452. https://doi.org/10.3390/s8042436

Badoux, A., Graf, C., Rhyner, J., Kuntner, R., & McArdell, B. W. (2009). A debris-flow alarm system for the alpine illgraben catchment: Design and performance. *Natural Hazards, 49*(3), 517-539. https://doi.org/10.1007/s11069-008-9303-x

Bai, T., & Tahmasebi, P. (2022). Attention-based LSTM-FCN for earthquake detection and location. *Geophysical Journal International, 228*(3), 1568-1576. https://doi.org/10.1093/gji/ggab401

Bellı, G., Walter, F., McArdell, B., Gheri, D., & Marchetti, E. (2022). Infrasonic and seismic analysis of debris-flow events at Illgraben (Switzerland): Relating signal features to flow parameters and to the seismo-acoustic source mechanism. *Journal of Geophysical Research: Earth Surface, 127*(6), e2021JF006576. https://doi.org/10.1029/2021jf006576

Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid earth geoscience. *Science, 363*(6433), eaau0323. https://doi.org/10.1126/science.aau0323

Breiman, L. (2001). Random forests. *Machine Learning, 45*(1), 5-32. https://doi.org/10.1023/a:1010933404324

Burtin, A., Hovius, N., McArdell, B., Turowski, J., & Vergne, J. (2014). Seismic constraints on dynamic links between geomorphic processes and routing of sediment in a steep mountain catchment. *Earth Surface Dynamics, 2*(1), 21-33. https://doi.org/10.5194/esurf-2-21-2014

Burtin, A., Hovius, N., & Turowski, J. M. (2016). Seismic monitoring of torrential and fluvial processes. *Earth Surface Dynamics, 4*(2), 285-307. https://doi.org/10.5194/esurf-4-285-2016

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In *Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining* (pp. 785-794). ACM. https://doi.org/10.1145/2939672.2939785

Chmiel, M., Walter, F., Wenner, M., Zhang, Z., McArdell, B. W., & Hibert, C. (2021). Machine learning improves debris flow warning [Dataset]. *Geophysical Research Letters, 48*(3), e2020GL090874. https://doi.org/10.1029/2020GL090874

Cofré, A., Marín, M., Pino, O. V., Galleguillos, N., Riquelme, S., Barrientos, S., & Yoma, N. B. (2022). End-to-end LSTM-based earthquake magnitude estimation with a single station. *IEEE Geoscience and Remote Sensing Letters, 19*, 1-5. https://doi.org/10.1109/lgrs.2022.3175108

Cook, K. L., & Dietze, M. (2022). Seismic advances in process geomorphology. *Annual Review of Earth and Planetary Sciences, 50*(1), 183-204. https://doi.org/10.1146/annurev-earth-032320-085133
Cook, K. L., Rekapalli, R., Dietze, M., Pilz, M., Cesca, S., Rao, N. P., et al. (2021). Detection and potential early warning of catastrophic flow events with regional seismic networks. *Science*, **374**(6563), 87–92. https://doi.org/10.1126/science.abj1227

Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). *Random forests* (pp. 157–175). Ensemble Machine Learning: Methods and Applications.

Dannemann Dugick, F., Koch, C., Berg, E., Arrowsmith, S., & Albert, S. (2023). A new decade in seismoacoustics (2010–2022). *Bulletin of the Seismological Society of America*, **113**(4), 1390–1423. https://doi.org/10.1785/0120220157

Dowling, C. A., & Santi, P. M. (2014). Debris flows and their toll on human life: A global analysis of debris-flow fatalities from 1950 to 2011. *Natural Hazards*, **71**(1), 203–227. https://doi.org/10.1007/s11069-013-0907-4

Du, J., Zhou, G. G., Tang, H., Turowski, J. M., & Cui, K. F. (2023). Classification of stream, hyperconcentrated, and debris flow using dimensional analysis and machine learning. *Water Resources Research*, **59**(2), e2022WR033242. https://doi.org/10.1029/2022wr033242

Farin, M., Mangeney, A., De Rosny, J., Toussaint, R., & Trinh, P.-T. (2018). Link between the dynamics of granular flows and the generated seismic signal: Insights from laboratory experiments. *Journal of Geophysical Research: Earth Surface*, **123**(6), 1407–1429. https://doi.org/10.1029/2017jf004296

Farin, M., Tsai, V. C., Lamb, M. P., & Allstadt, K. E. (2019). A physical model of the high-frequency seismic signal generated by debris flows. *Earth Surface Processes and Landforms*, **44**(13), 2529–2543. https://doi.org/10.1002/esp.4677

Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? *Journal of Machine Learning Research*, **15**(1), 3133–3181.

Feurer, M., & Hutter, F. (2019). Hyperparameter optimization. Automated machine learning: Methods, systems, challenges (pp. 3–33).

González, S., García, S., Del Ser, J., Rokach, L., & Herrera, F. (2020). A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities. *Information Fusion*, **64**, 205–237. https://doi.org/10.1016/j.infus.2020.07.007

Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. *Expert Systems with Applications*, **73**, 220–239. https://doi.org/10.1016/j.eswa.2016.12.035

Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. *IEEE Transactions on Knowledge and Data Engineering*, **15**(6), 1437–1447. https://doi.org/10.1109/tkde.2003.1245283

Hammer, C., Beyreuther, M., & Ohnberger, M. (2012). A seismic-event spotting system for volcano fast-response systems. *Bulletin of the Seismological Society of America*, **102**(3), 948–960. https://doi.org/10.1785/0120110167

Hauke, J., & Kossowski, T. (2011). Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data. *Quaestiones Geographicae*, **30**(2), 87–93. https://doi.org/10.2478/v10117-011-0021-1

Hibert, C., Michéa, D., Provost, F., Malet, J., & Geertsema, M. (2019). Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska. *Geophysical Journal International*, **219**(2), 1138–1147. https://doi.org/10.1093/gji/ggz354

Hibert, C., Noël, F., Toe, D., Talib, M., Desrues, M., Wyser, E., et al. (2024). Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate. *Earth Surface Dynamics*, **12**(3), 641–656. https://doi.org/10.5194/esurf-12-641-2024

Hibert, C., Provost, F., Malet, J.-P., Maggi, A., Stumpf, A., & Ferrazzini, V. (2017). Automatic identification of rockfalls and volcano-tectonic earthquakes at the piton de la Fournaise volcano using a random forest algorithm. *Journal of Volcanology and Geothermal Research*, **340**, 130–142. https://doi.org/10.1016/j.jvolgeores.2017.04.015

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. *Neural Computation*, **9**(8), 1735–1780.

Hübl, J., Schimmel, A., Kogelnig, A., Suriñach, E., Vilajosana, I., & McArdell, B. (2013). A review on acoustic monitoring of debris flow. *International Journal of Safety and Security Engineering*, **3**(2), 105–115. https://doi.org/10.2495/safe-v3-n2-105-115

Hürlimann, M., Coviello, V., Bel, C., Guo, X., Berti, M., Graf, C., et al. (2019). Debris-flow monitoring and warning: Review and examples. *Earth-Science Reviews*, **199**, 102981. https://doi.org/10.1016/j.earscirev.2019.102981

Hutter, K., Svendsen, B., & Rickenmann, D. (1994). Debris flow modeling: A review. *Continuum Mechanics and Thermodynamics*, **8**, 1–35. https://doi.org/10.1007/bf01175749

Iverson, R. M. (1997). The physics of debris flows. *Reviews of Geophysics*, **35**(3), 245–296. https://doi.org/10.1029/97rg00426

Jakob, M., Hungr, O., & Jakob, D. M. (2005). *Debris-flow hazards and related phenomena* (Vol. 739). Springer.

John, G. H., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. In *Machine learning proceedings 1994* (pp. 121–129). Elsevier.

Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2017). LSTM fully convolutional networks for time series classification. *IEEE Access*, **6**, 1662–1669. https://doi.org/10.1109/access.2017.2779939

Karimpouli, S., Caus, D., Grover, H., Martínez-Garzón, P., Bohnhoff, M., Beroza, G. C., et al. (2023). Explainable machine learning for labquake prediction using catalog-driven features. *Earth and Planetary Science Letters*, **622**, 118383. https://doi.org/10.1016/j.epsl.2023.118383

Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. In *2014 science and information conference* (pp. 372–378).

Kotsiantis, S. (2011). Feature selection for machine learning classification problems: A recent overview. *Artificial Intelligence Review*, **42**(1), 157–176. https://doi.org/10.1007/s10462-011-9230-1

Krawczyk, B. (2016). Learning from imbalanced data: Open challenges and future directions. *Progress in artificial intelligence*, **5**(4), 221–232. https://doi.org/10.1007/s13748-016-0094-0

Lagarde, S., Dietze, M., Hammer, C., Zeckra, M., Voigtländer, A., Illien, L., et al. (2023). Rock slope failure preparation paced by total crack boundary length. *Communications Earth & Environment*, **4**(1), 201. https://doi.org/10.1038/s43247-023-00851-0

Lai, V. H., Tsai, V. C., Lamb, M. P., Ulizio, T. P., & Beer, A. R. (2018). The seismic signature of debris flows: Flow mechanics and early warning at Montecito, California. *Geophysical Research Letters*, **45**(11), 5528–5535. https://doi.org/10.1029/2018gl077683

Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., et al. (2015). Environmental seismology: What can we learn on earth surface processes with ambient noise? *Journal of Applied Geophysics*, **116**, 62–74. https://doi.org/10.1016/j.jappgeo.2015.02.001

Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017). Temporal convolutional networks for action segmentation and detection. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 156–165).

Maggi, A., Ferrazzini, V., Hibert, C., Beauducel, F., Boissier, P., & Amemoutou, A. (2017). Implementation of a multistation approach for automated event classification at piton de la Fournaise volcano. *Seismological Research Letters*, **88**(3), 878–891. https://doi.org/10.1785/0220160189

Marchetti, E., Walter, F., Barfucci, G., Genco, R., Wenner, M., Ripepe, M., et al. (2019). Infrasound array analysis of debris flow activity and implication for early warning. *Journal of Geophysical Research: Earth Surface*, **124**(2), 567–587. https://doi.org/10.1029/2018jf004785
McArdell, B. W. (2016). Field measurements of forces in debris flows at the illgraben: Implications for channel-bed erosion. *International Journal of Erosion Control Engineering*, 9(4), 194-198. https://doi.org/10.13101/ijece.9.194

McArdell, B. W., Bartelt, P., & Kowalski, J. (2007). Field observations of basal forces and fluid pore pressure in a debris flow. *Geophysical Research Letters*, 34(7), L07406. https://doi.org/10.1029/2006GL029183

McArdell, B. W., & Hirschberg, J. (2020). Illgraben debris-flow characteristics 2019-2022 [Dataset]. EnviDat. https://doi.org/10.16904/envidat.173

McArdell, B. W., Hirschberg, J., Graf, C., Boss, S., & Badoux, A. (2023). Illgraben debris-flow characteristics 2019-2022 [Dataset]. EnviDat. https://doi.org/10.16904/envidat.378

Mousavi, S. M., & Beroza, G. C. (2023). Machine learning in earthquake seismology. *Annual Review of Earth and Planetary Sciences*, 51(1), 105-129. https://doi.org/10.1146/annurev-earth-071822-100323

Myers, L., & Sirois, M. J. (2004). Spearman correlation coefficients, differences between. *Encyclopedia of Statistical Sciences*, 12.

Newcomb, S. (1881). Note on the frequency of use of the different digits in natural numbers. *American Journal of Mathematics*, 4(1), 39-40. https://doi.org/10.2307/2369148

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., et al. (2019). Pytorch: An imperative style, high-performance deep learning library. *Advances in Neural Information Processing Systems*, 32.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. *Journal of Machine Learning Research*, 12, 2825-2830.

Petley, D. (2023). The devastating 17 July 2023 debris flow at Quetame in Colombia. *American Geophysical Union Blogosphere*. Retrieved from https://blogs.agu.org/landslideblog/2023/07/19/quetame-1/. Accessed 20 July 2023.

Petley, D. (2024). The possible causes of the deadly 21 June 2024 SORTE debris flow in Switzerland. *Eos American Geophysical Union*. Retrieved from https://eos.org/thelandslideblog/sorte-debris-flow-2

Ponziani, M., Ponziani, D., Giorgi, A., Stevenin, H., & Ratto, S. (2023). The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall. *Natural Hazards*, 117(1), 143-162. https://doi.org/10.1007/s11069-023-05853-x

Porter, R., Joyal, T., Beers, R., Youberg, A., Loverich, J., Schenk, E., & Robichaud, P. R. (2023). Characterization of environmental seismic signals in a post-wildfire environment: Examples from the museum fire, az. *Journal of Geophysical Research: Earth Surface*, 128(7), e2022JF006962. https://doi.org/10.1029/2022JF006962

Provost, F., Hibert, C., & Malet, J.-P. (2017). Automatic classification of endogenous landslide seismicity using the random forest supervised classifier. *Geophysical Research Letters*, 44(1), 113-120. https://doi.org/10.1002/2016gl070709

Rainio, O., Teuho, J., & Klén, R. (2024). Evaluation metrics and statistical tests for machine learning. *Scientific Reports*, 14(1), 6086. https://doi.org/10.1038/s41598-024-56706-x

Sambridge, M., Tkalčić, H., & Jackson, A. (2010). Benford's law in the natural sciences. *Geophysical Research Letters*, 37(22), L22301. https://doi.org/10.1029/2010gl044830

Santi, P., Hewitt, K., VanDine, D., & Barillas Cruz, E. (2011). Debris-flow impact, vulnerability, and response. *Natural Hazards*, 56(1), 371-402. https://doi.org/10.1007/s11069-010-9576-8

Seabold, S., & Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. In *9th python in science conference*.

Sherstinsky, A. (2020). Fundamentals of recurrent neural network ( rnn ) and long short-term memory ( lstm ) network. *Physica D: Nonlinear Phenomena*, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306

Shokouhi, P., Girkar, V., Rivière, J., Shreedharan, S., Marone, C., Giles, C. L., & Kifer, D. (2021). Deep learning can predict laboratory quakes from active source seismic data. *Geophysical Research Letters*, 48(12), e2021GL093187. https://doi.org/10.1029/2021g1093187

Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2020). A review of unsupervised feature selection methods. *Artificial Intelligence Review*, 53(2), 907-948. https://doi.org/10.1007/s10462-019-09682-y

Takahashi, T. (2007). Debris flow: Mechanics, prediction and countermeasures. Taylor & Francis.

Tiranti, D., Cremonini, R., Marco, F., Gaeta, A. R., & Barbero, S. (2014). The defense (debris flows triggered by storms-nowcasting system): An early warning system for torrential processes by radar storm tracking using a geographic information system (GIS). *Computers & Geosciences*, 70, 96-109. https://doi.org/10.1016/j.cageo.2014.05.004

Trajman, A., & Luiz, R. (2008). Mcnemar chi² test revisited: Comparing sensitivity and specificity of diagnostic examinations. *Scandinavian Journal of Clinical and Laboratory Investigation*, 68(1), 77-80. https://doi.org/10.1080/00365510701666031

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. *Artificial Intelligence Review*, 53(8), 5929-5955. https://doi.org/10.1007/s10462-020-09838-1

Walter, F., Burtin, A., McArdell, B. W., Hovius, N., Weder, B., & Turowski, J. M. (2017). Testing seismic amplitude source location for fast debris-flow detection at Illgraben, Switzerland. *Natural Hazards and Earth System Sciences*, 17(6), 939-955. https://doi.org/10.5194/nhess-17-939-2017

Wang, C.-Y., Huang, T.-C., & Wu, Y.-M. (2022). Using LSTM neural networks for onsite earthquake early warning. *Seismological Society of America*, 93(2A), 814-826. https://doi.org/10.1785/0220210197

Wenner, M., Hibert, C., Meier, L., & Walter, F. (2020). Near real-time automated classification of seismic signals of slope failures with continuous random forests. *Natural Hazards and Earth System Sciences Discussions*, 2020, 1-23.

Werner de Vargas, V., Schneider Aranda, J. A., dos Santos Costa, R., da Silva Pereira, P. R., & Victória Barbosa, J. L. (2023). Imbalanced data preprocessing techniques for machine learning: A systematic mapping study. *Knowledge and Information Systems*, 65(1), 31-57. https://doi.org/10.1007/s10115-022-01772-8

Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. *Neurocomputing*, 415, 295-316. https://doi.org/10.1016/j.neucom.2020.07.061

Yang, Y., Chen, G., Li, Y., Meng, X., Chong, Y., Bian, S., et al. (2024). Characterization of stream, hyperconcentrated and debris flows from seismic signals: Insights into sediment transport mechanisms and flow dynamics. *Journal of Geophysical Research: Earth Surface*, 129(4), e2023JF007527. https://doi.org/10.1029/2023JF007527

Zhang, Z., Walter, F., McArdell, B. W., Wenner, M., Chmiel, M., de Haas, T., & He, S. (2021). Insights from the particle impact model into the high-frequency seismic signature of debris flows. *Geophysical Research Letters*, 48(1), e2020GL088994. https://doi.org/10.1029/2020gl088994

Zhao, Y., Meng, X., Qi, T., Li, Y., Chen, G., Yue, D., & Qing, F. (2022). Ai-based rainfall prediction model for debris flows. *Engineering Geology*, 296, 106456. https://doi.org/10.1016/j.enggeo.2021.106456

Zhou, Q. (2025). Supporting material for “Enhancing debris flow warning via machine learning feature reduction and model selection” [Dataset]. *Zenodo*. https://doi.org/10.5281/zenodo.15020368
Zhou, Q., Tang, H., Turowski, J. M., Braun, J., Yang, C.-J., Dietze, M., et al. (2024). Benford's law as debris flow detector in seismic signals. *Journal of Geophysical Research: Earth Surface*, 129(9), e2024JF007691. https://doi.org/10.1029/2024jf007691

Zhou, X., Wen, H., Zhang, Y., Xu, J., & Zhang, W. (2021). Landslide susceptibility mapping using hybrid random forest with geodetector and rfe for factor optimization. *Geoscience Frontiers*, 12(5), 101211. https://doi.org/10.1016/j.gsf.2021.101211

Zurich, S. S. S. A. E. (2012). 9s - Temporary deployments in Switzerland associated with landslides [Dataset]. ETH Zurich. https://doi.org/10.12686/SED/NETWORKS/XP