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# Rockfall Forecasting using Ensemble Deep Learning and Temporal Gradient-Based Explanations

Sabrine Bouaziz, Christophe Lin-Kwong-Chon, David Amitrano, Nicolas

Méger

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## To cite this version:

Sabrine Bouaziz, Christophe Lin-Kwong-Chon, David Amitrano, Nicolas Méger. Rockfall Forecasting using Ensemble Deep Learning and Temporal Gradient-Based Explanations. International Conference on Data Mining (ICDM), Workshop of Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications, IEEE, Nov 2025, Washington, D.C., United States. hal-05311348

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https://hal.science/hal-05311348v1

Submitted on 13 Oct 2025

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# Rockfall Forecasting using Ensemble Deep Learning and Temporal Gradient-Based Explanations

*Preprint version – Submitted to IEEE ICDM Conference 2025*

Sabrine Bouaziz¹, Christophe Lin-Kwong-Chon¹, Nicolas Méger¹, David Amitrano²

¹Université Savoie Mont Blanc, Polytech Annecy-Chambéry, LISTIC, Annecy, France
{sabrine.bouaziz, christophe.lin-kwong-chon, nicolas.meger}@univ-smb.fr

²Univ. Grenoble Alpes, CNRS, IRD, ISTerre, Grenoble, France

david.amitrano@univ-grenoble-alpes.fr

***Abstract***—Rockfalls pose a significant risk for infrastructures and human activities. Accurate forecasting is therefore crucial for prevention. While current methods rely on expert hypotheses, AI models, particularly explainable ones, offer a promising way to improve the accuracy of forecasts and provide transparent, trustworthy results. In this paper, an ensemble of multisource neural networks based on InceptionTime modules whose forecasts are explained using a temporal Grad-CAM approach is being developed for the Mont Saint-Eynard cliff, showcasing the potential of AI in this area.

***Index Terms***—Time series, Rockfall, Forecasting, Ensemble Learning, InceptionTime, Grad-CAM.

## I. INTRODUCTION

Rockfalls occurring near anthropogenic activities pose significant risks, including the destruction of infrastructure, threats to human safety, and the closure of roads, often resulting in substantial economic losses. When cliff avoidance is not possible, two main strategies can be implemented. The first one involves the installation of protective structures like sheds or nets, which are relatively expensive. The second strategy focuses on forecasting rockfall occurrences, enabling preventive measures to be taken. In this case, it is essential to ensure both reliable and accurate forecasts to reduce the amount of false alarms.

Rockfall occurrence depends on many factors, such as historical events, the geological context, structural characteristics, the morphological history, or hydrogeological and climatic information [1]. Meteorological conditions such as rainfall, temperature variation or freeze-thaw cycles are well known to be correlated to such events. D'Amato et al. [2] studied the Mont Saint-Eynard limestone cliff using annual terrestrial laser scanning and photographic survey over a period of 2.5 years. The authors identified a multiplication factor of 7 during freeze-thaw episodes and a factor of 26 during mean rainfall intensity higher than 5 mm/h. Delonca et al. [3] studied different cliff sites and also showed that rockfalls can be driven by rainfalls or daily temperature differences.

All of these studies are conducted by experts who manually test their own hypotheses. This restricts the discovery of

forecast models to only tested hypotheses and the existing knowledge of the experts. To explore other models that could improve the accuracy of forecasts, artificial intelligence models must be considered. Training an explainable model is essential in order to provide trustworthy, transparent, and understandable forecasts to authorities and infrastructure managers.

In this paper, using standard machine learning techniques, an ensemble of multisource neural networks based on InceptionTime modules is built to forecast the rockfalls of the Mont Saint-Eynard cliff, which is located in the French Alps. Forecasts are explained by relying on a temporal-based Gradient-weighted Class Activation Mapping (Grad-CAM) approach. Experiments demonstrate the potential of the proposed approach and call for deepening research in such an applicative context.

## II. RELATED WORK

Machine Learning (ML) has become a key component for rockfall prediction and forecasting by utilizing a variety of data sources such as seismic signals, laser point clouds and meteorological data. For example, a study developed an end-to-end ML framework using point clouds achieves high accuracy when detecting rockfall clusters despite unbalanced data [4]. Besides identifying rockfall occurrences in historical data, rockfalls can also be forecasted. Recent works on rockfall forecasting have moved away from statistical analysis and physics-based models, demonstrating that standard classification techniques can improve the predictive accuracy. Notably, in [5], Chanut et al. show that when paired with historical rockfall records, even basic inputs, such as rainfall data, can lead to competitive forecasts. In more detail, using four different machine learning methods (KNN, logistic regression, bagged trees, and deep neural networks), the authors forecast the next day's rockfall events along National Road 1 at La Réunion. The performances of these models are similar, if not better than, those obtained with expert rules based on the cumulated rainfall over 24 hour.

As ML models become increasingly complex, explainability has become a key concern, particularly when dealing with multisource time series. Recent advances in gradient-based methods aim to highlight which input feature and time window

<footnote>This research was funded by the French National Research Agency (ANR) under the project C2R-IA (https://anrc2ria.fr/), grant ANR-22-CE56-0005-06.</footnote>
impact the prediction of a model, such as a multilayer Grad-CAM [6] that aggregates saliency maps across multiple convolutional layers to achieve better explainability, or a fusion-based Grad-CAM [7] that applies Grad-CAM to electrocardiogram signals transformed into the temporal and frequency domains and performs a dynamic fusion weighted by Grad-CAM. Also, Zhang et al. [8] recently proposed a Reordering-Enhanced Grad-CAM algorithm with high-dimensional financial time series data where they treat sliding window data as pseudo-images to enhance interpretability and identify critical features through high-dimensional data.

However, despite these advanced works, no model incorporating an explainable approach dedicated to rockfall forecasting has been proposed. While post-hoc explanation methods such as SHAP, LIME, and Grad-CAM have been used in the field of landslide detection [9]–[11], they have not been used in rockfall forecasting models. Additionally, though KNN and logistic regression-based classifiers were used in [5], their ante-hoc explanations have not been assessed to check that they provide the expected added value in terms of trust and transparency for risk management.

In this paper, an approach similar to the one adopted by Oprea et al. [12] to classify breath ventilation data is adopted. In [12], flow and pressure time series are used in distinct convolution branches and then merged at a later stage. A Grad-CAM-based technique is employed to extract both combined and separated source explanations. Regarding our proposal, a multisource forecasting architecture that processes each source using a dedicated convolution branch before merging extracted features to forecast rockfalls is adopted. Using a Grad-CAM technique, explanations are provided for each source individually, as explanations based on combined features can be difficult to interpret, which undermines experts’ trust. In addition to the application area, this work differs from that described in [12] in that, rather than relying on a single neural network, a neural network ensemble is built to achieve higher and more robust performance [13]. Finally, the Grad-Cam explanations of each majority neural network are aggregated to provide a single temporal Grad-Cam explanation for each forecast and each source.

### III. METHODOLOGY

#### A. Data

This study deals with the Mont Saint-Eynard, a limestone cliff culminating at 1379 meters, near the town of Grenoble in the French Alps, known for its recurrent rockfall activity.

1) Data sources: Two main types of time series, i.e., meteorological measurements and rockfall events data, are exploited from 2014 to 2024.

a) Meteorological data: They are provided by METEO France weather stations and are used to characterize the climatic conditions influencing the rockfalls of the study site. Only stations located within a 5 km radius of the study site (see Fig. 1) are selected. Rainfall data were collected from the Grenoble-LVD station, which is the closest one. This time

<figure><img src="image_4.png" /><figcaption>Fig. 1: Location of the meteorological station near the study site. (Source: Google Maps, 2025)</figcaption></figure>

series was recorded with an hourly resolution, which allows modelling at a high temporal resolution.

In order to enrich the discrete information of rainfall, benefit from experts’ knowledge, and improve explainability, a derived feature, namely Water Charge, was calculated from the rainfall time series. This representation follows the method of antecedent daily rainfall model [14]. It captures the dynamics of water accumulation and dissipation in the cliff, using an exponential decay model, based on the following formula:

$$ \Delta H(t + 1) = \text{Rainfall}(t) + H(t)e^{-\Delta t/\lambda} \quad (1) $$

where λ represents the decay coefficient which control the duration of previous rainfall’s persistence. When λ is small, it causes a rapid reduction in $H(t)$ while when it is greater, it leads to a slower decay and prolonged ground wetness. This feature shows that the persistence of water within the rock mass affects rockfall triggering in addition to immediate rainfall. The decay coefficient was set to 0.2, empirically based on experimental analysis of wet and dry ground in relation to rockfall occurrence probability.

So, from modeling perspective, adding the water charge data provides the network with a memory of past precipitation and complements the rainfall data that presents the short-term peaks to capture both immediate and delayed controls on rockfall occurrence.

b) Rockfall catalogue obtained through seismic monitoring: The time series of rockfall events is an event-based dataset obtained using template matching algorithms developed at the ISTerre laboratory [15] to process seismic data. It contains 668 rockfalls recorded at hourly resolution over the 10-year period. The frequency of rockfalls varies according to seasons and thus weather conditions. The following histogram illustrates this behavior (see Fig. 2). Finally, a total of 475 days are affected by rockfalls, indicating that the daily empirical probability of at least one rockfall event is 12.17%.

2) Data preparation:

a) Cross-validation using yearly groups: To ensure robust evaluation and build a neural network ensemble, we use a k-fold cross-validation technique based on ten civil years from 2014 to 2023. For each fold, we leave one year out for testing
<figure><img src="image_5.png" /><figcaption>Fig. 2: Average number of rockfalls by month over the period [01-01-2014 : 31-12-2024]</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3: Cross-correlations between rockfall and rainfall.</figcaption></figure>

and apply a cross-validation on the remaining years with one year for validation and the rest for training. This nested cross-validation setup ensures that a rockfall event is only used once for training, validation, or testing. It also ensures that all seasons are present in each test and validation dataset, as well as balances the number of samples between these two subsets. Finally, this cross-validation approach enables the learning of 90 different neural networks, given that there are 10 outer folds and 9 inner folds. These networks make up the ensemble that serves as our forecasting model.

b) *Learning examples construction:* Let us define $t_0$ as an observation time, i.e., each midnight, and two associated observation windows, also termed horizons: the Retrospective Horizon $RH = [t_{start}, t_0)$ and the Forecasting Horizon $FH = [t_0, t_{end}]$. Learning examples represent forecasts and are built as follows: each midnight serves once as $t_0$ to generate a single forecast. Its corresponding learning data consists of: 1) the learning variables, i.e., the normalized rainfall and water charge levels observed over $RH$, and 2) the variable to be predicted, i.e., a label indicating whether or not at least one rockfall was observed over $FH$ in the seismic data.

A cross-correlation analysis between rainfall and rockfall occurrence (Fig. 3) revealed that the strongest dependencies are concentrated within the 14 days preceding a rockfall event.

Based on this empirical observation and after studying the basic statistics of the dataset with the experts, the time span of $RH$ was set to 336 hours (14 days) and that of $FH$ to 1 and 3 days. This results in two different datasets, one for each $FH$ time span value. Observation times for which $RH$ and $FH$ are not fully known, either because $RH$ starts before the available data or because $FH$ ends after them, were rejected to avoid making any assumptions when building learning examples.

The least favorable rejection scheme, i.e., 14 days for $RH$ and 3 days for $FH$, is applied to build a single dataset that is then used to assess all $FH$ time spans in a fair manner. Consequently, the dataset contains 3652 forecasts since full civil years are considered for our cross-validation strategy.

## B. Predictive Model and Feature Fusion

1) *Deep architecture:* A deep convolutional architecture is adopted based on the InceptionTime model [16]. This architecture is designed to handle our two data sources: the normalized rainfall and water charge levels. Each of these sources is represented by a dedicated vector of 336-hours $RH$ time span. The model uses two distinct branches to process these vectors separately, enabling features specific to each data source to be learnt. Each branch is composed of two Inception blocks. Each block contains three Inception modules. These modules employ multi-scale convolutional filters whose kernel sizes are the ones adopted in [16]: (9 × 1), (19 × 1) and (39 × 1) to extract temporal features at multiple resolutions. This configuration captures short-, medium-, and long-term dependencies.

In this proposed architecture, an attention gate, inspired by the mechanism introduced in Attention U-Net [17] is inserted within each Inception block, positioned before the residual connection, allowing the model to adaptively weight the features produced by the Inception modules according to their relevance. Unlike attention mechanisms, typically used to model interpretability, our implementation focuses on improving feature selectivity, mitigating noise, and enhancing overall model performance. This method allows our network to concentrate on the most instructive temporal patterns for every data source thanks to this mechanism.

To enable the model to integrate the temporal features learnt for each source, the outputs of each branch are concatenated before being mixed twice with two fully connected layers of sizes (128 × 1) and (2 × 1); the latter being the final classification layer. In other words, late fusion is used to extract the highest-level features of each source and exploit them together. The overall architecture is illustrated in Fig. 4.

During training, a weighted cross-entropy loss was used to handle class imbalance in our dataset. This loss increases the penalty for misclassifying the minority class samples (rockfall), forcing the model to prioritize learning these patterns. We employ a batch size of 32 with shuffling to ensure a diverse representation of samples within each training iteration.

## C. Ensemble aggregation via hard voting

Since our evaluation relies on a cross-validation as described previously (10 outer folds × 9 inner folds), a total of 90 models are trained. These models together form the ensemble that serves as the final forecasting model. A *hard voting* strategy [13] is applied: each model outputs a binary label (*rockfall* or *no rockfall*) and the final predicted class is assigned to the one receiving the majority of votes (more than half of the models). Such an ensemble is expected to produce reliable results than relying on a single trained model [13]. Its performance is
<figure><img src="image_7.png" /><figcaption>Fig. 4: Architecture of the multisource InceptionTime model.</figcaption></figure>

assessed over the full year 2024, which is kept apart as a blind test set, i.e., it is not used by the cross-validation training process.

### D. Explainability Approach

1) *Grad-CAM explanations for a single neural network:* To get a better understanding of the forecasts provided by a single neural network, for each source, the parts of the input data that contribute most to the prediction of rockfall are highlighted using a post-hoc method based on Grad-CAM. Originally designed for visual CNN [18] on 2D convolutional kernels, Grad-CAM has been adapted in our work to highlight temporal segments of each source input with 1D convolutional kernels [19]. To achieve this, Grad-CAM was computed on the output of the last Inception Module of each Inception branch. At this strategic location, it captures high-level temporal features immediately before the concatenation, providing a discriminating capacity for identifying influential time steps. It enables us to identify which temporal segments among the 336 hours are the most influential for each rockfall forecast and each source.

2) *Grad-CAM aggregation across the ensemble:* Since our evaluation relies on an ensemble of 90 models trained through cross-validation, Grad-CAM maps are first computed separately for each neural network. More precisely, for each forecast and each source, we retain the explanations of each majority neural network, i.e., the networks that vote for the actual forecast. These explanations are then simply averaged to provide end-users with a single ensemble explanation for each forecast and each source. To better understand the behavior of the ensemble for both accurate and erroneous decisions, the following four standard representative scenarios are considered: (i) correctly forecasting class Rockfall, (ii) correctly forecasting class No Rockfall, (iii) misclassified instances of class Rockfall, and (iv) misclassified instances of class No Rockfall. The explanations obtained for the forecasts of the ensemble are first sorted according to each one of the scenarios. The ensemble explanations associated with a given scenario are then averaged for each source to obtain

the average Grad-CAM characterizing the functioning of the ensemble for the considered scenario.

All scripts used for preprocessing and model training are available at *our GitHub repository*.

## IV. RESULTS

### A. Performance of the deep architecture

To evaluate the performance of the proposed deep architecture, the precision, recall, and F1-score measures obtained during the cross-validation learning phase over the period 2014–2023 are summarized in Table I. It highlights both the overall predictive performance and the consistency of the models learned across the different folds.

### B. Ensemble performance

To evaluate the performance of the ensemble, the weighted accuracy, F1-score, precision and class accuracy (i.e., recall) measures were computed over 2024. They are reported in Table II along with the results obtained for an ensemble whose only difference lies in the fact that no attention gates were used. Another comparison is proposed by providing the performance results of an expert rule representing the vast majority of the rules available in the literature available for limestone cliffs and assumed to be valid for the Mont Saint-Eynard cliff by experts. This expert rule is expressed as follows: "At least one rockfall occurs within a future horizon of 1 to 3 days following a period of three consecutive days with at least one hour of rainfall each day".

We observe in Table II that as the *FH* increases, the recall of the InceptionTime-based with or without the attention gate also increases, which confirms the daily empirical probability. In any case, one can expect approximately one rockfall every seven days, leading to an increase of rockfall recall and a decrease of no rockfall. For the sake of clarity, only the one-day *FH* case will be detailed in the remainder of this paper.

More precisely, regarding class-specific metrics, the recall of the rockfall class achieved by the attention gate InceptionTime-based ensemble is nearly twice that of the expert rule for a one-day *FH*.

Confusion matrices in Tables III and IV highlight the effectiveness of the InceptionTime-based models with a true positive detection 2 times higher than the expert rule, indicating a significant improvement in the detection of true rockfall events. The implemented attention gates do not improve rockfall prediction; however, they help reduce false alarms, decreasing from 100 to 79, which corresponds to a 20% reduction. The expert rule results in slightly fewer false negatives, 56 compared to 79 for the attention gate InceptionTime-based model.

For each test sample, we count the number of models predicting class 0 and class 1 and compute the average and standard deviation of this count across all samples belonging to the four scenarios presented above. This provides a measure of the ensemble confidence for each scenario. The results are summarized in Table V. We notice that on average 59.17% of the models agree on the final prediction across the four
<table><caption>TABLE I: Cross-validation performance of the 90 models (2014–2023), reported as mean ± standard deviation of per-class precision, recall and F1-score for different forecasting horizons *FH*.</caption><thead><tr><th rowspan="2">Class</th><th colspan="2">Precision</th><th colspan="2">Recall</th><th colspan="2">F1-score</th></tr><tr><th>FH = 1</th><th>FH = 3</th><th>FH = 1</th><th>FH = 3</th><th>FH = 1</th><th>FH = 3</th></tr></thead><tbody><tr><th colspan="7" style="text-align: left; font-weight: bold;">InceptionTime</th></tr><tr><td>Rockfall</td><td>0.155 ± 0.086</td><td>0.294 ± 0.131</td><td>0.436 ± 0.103</td><td>0.399 ± 0.141</td><td>0.228 ± 0.082</td><td>0.338 ± 0.117</td></tr><tr><td>No Rockfall</td><td>0.897 ± 0.053</td><td>0.735 ± 0.122</td><td>0.655 ± 0.066</td><td>0.638 ± 0.117</td><td>0.757 ± 0.083</td><td>0.683 ± 0.043</td></tr><tr><th colspan="7" style="text-align: left; font-weight: bold;">InceptionTime with attention</th></tr><tr><td>Rockfall</td><td>0.166 ± 0.080</td><td>0.311 ± 0.143</td><td>0.422 ± 0.133</td><td>0.432 ± 0.111</td><td>0.238 ± 0.079</td><td>0.361 ± 0.116</td></tr><tr><td>No Rockfall</td><td>0.898 ± 0.058</td><td>0.726 ± 0.113</td><td>0.685 ± 0.098</td><td>0.610 ± 0.093</td><td>0.777 ± 0.070</td><td>0.662 ± 0.065</td></tr></tbody></table>

<table><caption>TABLE II: Per-class precision, recall and F1-score for different forecasting horizons *FH* on the full year 2024 using hard voting across the 90 models.</caption><thead><tr><th rowspan="2">Class</th><th colspan="2">Precision</th><th colspan="2">Recall</th><th colspan="2">F1-score</th></tr><tr><th>FH = 1</th><th>FH = 3</th><th>FH = 1</th><th>FH = 3</th><th>FH = 1</th><th>FH = 3</th></tr></thead><tbody><tr><th colspan="7" style="text-align: left; font-weight: bold;">InceptionTime</th></tr><tr><td>Rockfall</td><td>0.166</td><td>0.384</td><td><strong>0.392</strong></td><td>0.338</td><td>0.233</td><td>0.360</td></tr><tr><td>No Rockfall</td><td>0.866</td><td><strong>0.685</strong></td><td>0.667</td><td>0.726</td><td>0.754</td><td>0.705</td></tr><tr><th colspan="7" style="text-align: left; font-weight: bold;">InceptionTime with attention</th></tr><tr><td>Rockfall</td><td>0.202</td><td>0.347</td><td><strong>0.392</strong></td><td><strong>0.423</strong></td><td><strong>0.266</strong></td><td><strong>0.381</strong></td></tr><tr><td>No Rockfall</td><td><strong>0.877</strong></td><td>0.673</td><td>0.737</td><td>0.598</td><td>0.801</td><td>0.633</td></tr><tr><th colspan="7" style="text-align: left; font-weight: bold;">Expert rule (1 model)</th></tr><tr><td>Rockfall</td><td>0.164</td><td>0.268</td><td>0.215</td><td>0.152</td><td>0.186</td><td>0.193</td></tr><tr><td>No Rockfall</td><td>0.859</td><td>0.644</td><td>0.813</td><td>0.786</td><td><strong>0.835</strong></td><td>0.707</td></tr></tbody></table>

<table><caption>TABLE III: Confusion matrix for the expert rule with a one-day *FH* on the year 2024.</caption><thead><tr><th rowspan="2">Truth</th><th colspan="2">Predicted</th><th rowspan="2">Total</th></tr><tr><th>Rockfall</th><th>No Rockfall</th></tr></thead><tbody><tr><td>Rockfall</td><td>11</td><td>40</td><td>51</td></tr><tr><td>No Rockfall</td><td>56</td><td>245</td><td>301</td></tr><tr><td><strong>Total</strong></td><td>67</td><td>285</td><td>352</td></tr></tbody></table>

<table><caption>TABLE IV: Confusion matrix for the attention gate InceptionTime-based ensemble with a one-day *FH* on the year 2024. The values in parentheses correspond to the InceptionTime-based ensemble without attention.</caption><thead><tr><th rowspan="2">Truth</th><th colspan="2">Predicted</th><th rowspan="2">Total</th></tr><tr><th>Rockfall</th><th>No Rockfall</th></tr></thead><tbody><tr><td>Rockfall</td><td>20 (20)</td><td>31 (31)</td><td>51 (51)</td></tr><tr><td>No Rockfall</td><td><strong>79</strong> (100)</td><td><strong>222</strong> (201)</td><td>301 (301)</td></tr><tr><td><strong>Total</strong></td><td>99 (120)</td><td>253 (232)</td><td>352</td></tr></tbody></table>

scenarios. The ensemble tends to be more confident when predicting class 0 (no rockfall), which is consistent with the lower rate of missed detection (31) compared to the expert rule (40).

Finally, the balanced accuracy [20] is reported for both the attention gate InceptionTime-based ensemble (obtained via hard voting across all models) and the expert rule. It is defined with Equation 2 where *TP* is the number of True Positives, i.e., the number of forecasts of scenario (i) and noted |(i)|, *TN* is the number of True Negatives, i.e., |(ii)|, *FP* is number

<table><caption>TABLE V: Average number of attention gate InceptionTime-based model votes per class for each scenario.</caption><thead><tr><th>Scenario</th><th>Class 0 votes</th><th>Class 1 votes</th></tr></thead><tbody><tr><td>i: 1/1</td><td>35.4 ± 1.6</td><td>54.6 ± 1.6</td></tr><tr><td>ii: 0/0</td><td>50.6 ± 3.4</td><td>39.4 ± 3.4</td></tr><tr><td>iii: 0/1</td><td>54.4 ± 2.0</td><td>35.6 ± 2.0</td></tr><tr><td>iv: 1/0</td><td>36.6 ± 2.9</td><td>53.4 ± 2.9</td></tr></tbody></table>

of False Positives, i.e., |(iv)|, and *FN* is number of False Negatives, i.e. |(iii)|.

$$ \text{Balanced Accuracy} = \frac{1}{2} \left( \frac{TP}{TP + FN} + \frac{TN}{TN + FP} \right) \quad (2) $$

The AI ensemble with attention gate achieves a better average balanced accuracy of 56% and 51% for *FH* spanning over 1 and 3 days respectively, whereas, the expert rule obtains a balanced accuracy of 51% and 45% for the same horizons.

### C. Explainability

To illustrate the explainability workflow described in section III.D, Fig. 5 shows an example of a rainfall signal where the time series is overlaid with a color intensity reflecting the importance values of Grad-CAM. This visualization highlights which temporal segments of the signal are most influential for the forecasting, as identified here by two distinct trained models.

Fig. 6 then displays the corresponding average Grad-CAM across all models that correctly predict this same sample [23-11-2024 : 07-12-2024] (represented in Fig. 5), illustrating
<figure><img src="image_8.png" /><figcaption>Fig. 5: Example of Grad-CAM on rainfall input for the same correctly predicted Rockfall sample [23-11-2024 : 07-12-2024], obtained from two different trained single models (top: model A, bottom: model B).</figcaption></figure>

how ensemble averaging provides can be readable. For clarity, examples were limited here to rainfall source, but a similar workflow is run for the charge input source.

<figure><img src="image_9.png" /><figcaption>Fig. 6: Average Grad-CAM across all models that correctly predicted the Rockfall sample [23-11-2024 : 07-12-2024].</figcaption></figure>

Finally, both the averaged importance of Grad-CAM and input signals of each rain and water charge source over all samples within each of the four scenarios introduced in section III.D.2 are shown in the following figures to capture the general behavior of the ensemble on our test data. These average trends allow us to identify input patterns across all importance maps.

In scenario (*i*), where the model correctly forecasts rockfall events (see Fig. 7), the mean of Grad-CAM for inputs shows that the network's attention increases during the last 2 days, with a major implication in the final 25 hours, for both rainfall and water charge before the occurrence of the event. This trend is more pronounced in the rainfall branch, with importances 10 times higher than water charge importances. This suggests

<figure><img src="image_10.png" /><figcaption>(a) Rain branch Grad-CAM</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(b) Charge branch Grad-CAM</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(c) Rain samples</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(d) Charge samples</figcaption></figure>

<caption>Fig. 7: Scenario *i* (1/1): correctly predicting class Rockfall, Grad-CAM maps and input signals.</caption>
that the model relies heavily on recent rainfall activity and, to a lesser extent, on water charge to forecast rockfalls. For this scenario, the averaged rainfall signal on Fig. 7 (c) shows some low-intensity fluctuations followed by a noticeable rise from -25 hours with a maximum peak close to the event. We notice also a peak between -60 h and -50 h, where the Grad-CAM intensity is elevated but less pronounced compared to the to the activation observed near -25 hours. Rainfall intensities increased from 0.2 mm/h to values exceeding 0.6 mm/h just before the event, suggesting that sustained and intensified rainfall on the last two days appears to be a robust precursor for correctly forecasting rockfall. This trend is reflected in the charge signal, which gradually increases from 1 mm to more than 2 mm towards the event, indicating that the progressive accumulation of water on the surface leads up to a rockfall.

While the previous scenario highlighted how the model captures precursor patterns for rockfall events, Fig. 8 represents scenario (ii), which corresponds to correctly forecast no-rockfall events. The averaged Grad-CAMs for both inputs show a different temporal focus of the ensemble compared to the rockfall scenario (i). For both the rainfall and water charge branches, the importance values remain relatively stable throughout the window, followed by a decrease around -50 to -25 hours and a brief peak that appears just before the event for the rain branch. In the water charge branch, the highest importance is observed earlier in the time window from -280 to -50 hours with a slow decrease towards the event time. This suggests that the model relies on earlier water charge to recognize the absence of rockfall. The average rainfall signal (c) shows relatively low intensity (below 0.2 mm/h) fluctuations compared to the average rainfall in scenario (i) with a noticeable decrease close to the event, while the average water charge shows a smooth oscillatory behavior (below 0.1 mm), indicating no significant accumulation of water over time, and a decreasing trend towards the event. All this reflects the absence of extreme precipitation episodes or significant water charge accumulation, which is consistent with the scenario of correct prediction of no rockfall.

Overall, for no rockfall cases, even if a brief surge of importance is observed for rainfall in the last hours, the ensemble tends to spread its attention over a longer historical window for both rainfall and water charge, rather than focusing only on the last hours preceding the forecast, which is done for scenario (i).

We will now turn to cases where the model makes false forecasts to understand which factors it tends to highlight in such situations. In scenario (iii), where the model misclassified the class rockfall (see Fig. 9). Although the overall importance values remain low, the Rain branch exhibits relatively higher importance in the early hours (around -300 h) and the last 24 hours towards the event. The water charge branch is more uniformly low, with only a slight range from around -300 h to -200 h. The average of rainfall in these cases is highly fragmented, with isolated peaks rather than continuous episodes. Consequently, the derived charge signal remains weak, with only minor accumulations over the considered

<figure><img src="image_14.png" /><figcaption>(a) Rain branch Grad-CAM</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(b) Charge branch Grad-CAM</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(c) Rain samples</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(d) Charge samples</figcaption></figure>

Fig. 8: Scenario *ii* (0/0): correctly predicting class No Rockfall, Grad-CAM maps and input signals.
window. From the ensemble's perspective, these conditions give non-triggering rainfall. This scenario highlights an important limitation: the model tends to underestimate situations where rockfalls can occur in conditions of low or irregular precipitation. Either the model is not capturing the more complex precursors to rockfall, or these cases represent other rockfall triggers that were not included in the training data.

In scenario (iv), where the model forecasts rockfall class instead of no rockfall (see Fig. 10), the ensemble's attention in the rainfall branch is relatively low and stable across most of the time window (from -300 to -50 hours). There is a pronounced increase in the last nearly 25 hours before the forecasting time, suggesting that the ensemble is highly sensitive to recent rainfall activity when making incorrect forecasts of rockfall. The importance of the water charge branch shows a peak around -25 hours. The model provides a less pronounced attention to water charge than for rainfall. The average rainfall trend is represented by frequent fluctuations with very slightly higher peaks in the last hours, which make the model fail in forecasting the class rockfall. The average water charge signal reveals a regular oscillations with a slight rise at the end but without a clear trend. So, the model incorrectly forecast the rockfall due to its focus on recent rainfall peaks, even thought they are not extreme, and it also does not integrate long-term rainfall and water charge signals so that slight short-term fluctuations lead to misclassifications.

Overall, it should be noted that the phenomena exploited by the network when producing correct forecasts (scenarios (i) and (ii)) are supported by expert knowledge. This enables trust in the model to be established, in addition to providing transparency. Regarding false forecasts (scenarios (iii) and (iv)), it seems that these errors can be explained by the fact that no clear pattern or tendency can be extracted by the network, a situation that could also apply to experts.

Finally, from a user-friendly perspective, each scenario can be characterized by a standard Grad-CAM visualization where the importance level is depicted over the input signal using a dedicated color palette. Figure 11 gives the visualization.

V. CONCLUSION AND PERSPECTIVES

Rockfall forecasting is an emerging applicative field for machine learning models that is demanding both in terms of performance and also in terms of transparency to ensure trust from end-users. In this paper, it is shown that an ensemble of neural networks with standard multisource convolutional architectures can be exploited to reach performances that are similar to or better than those of expert-based rule. Moreover, the temporal explanations obtained for each forecast and each data source using a temporal adaptation of Grad-CAM convey useful information that matches experts’ knowledge.

In particular, when introducing an attention gate into the architecture, which balances the contribution of each input source, the network achieves better forecasting performance than experts. Still, performances could be improved. First, hyperparameters other than those of the literature should be tried to better conform with the applicative field. Additional

<figure><img src="image_18.png" /><figcaption>(a) Rain branch Grad-CAM</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(b) Charge branch Grad-CAM</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(c) Rain samples</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>(d) Charge samples</figcaption></figure>

Fig. 9: Scenario iii (0/1): misclassified instances of class Rockfall, Grad-CAM maps and input signals.
<figure><img src="image_22.png" /><figcaption>(a) Rain branch Grad-CAM</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(b) Charge branch Grad-CAM</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>(c) Rain samples</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>(d) Charge samples</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>(a) Grad-CAM over rainfall input for scenario *i*</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(b) Grad-CAM over water charge input for scenario *i*</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>(c) Grad-CAM over rainfall input for scenario *ii*</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(d) Grad-CAM over water charge input for scenario *ii*</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>(e) Grad-CAM over rainfall input for scenario *iii*</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>(f) Grad-CAM over water charge input for scenario *iii*</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>(g) Grad-CAM over rainfall input for scenario *iv*</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(h) Grad-CAM over water charge input for scenario *iv*</figcaption></figure>

<caption>Fig. 11: Standard Grad-CAM visualization for each scenario and each source.</caption>

input sources such as temperature time series should be incorporated since it is known that it can be a key parameter driving rockfalls. We also plan to investigate recurrent architectures, which can better capture temporal dependencies and further improve forecasting performance. All of these possible improvements ground our future work directions to simultaneously enhance model accuracy and explainability.

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