E3S Web of Conferences 415, 07012 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341507012

A regional early warning system for debris flows
Michel Ponziani1*, Denise Ponziani1, Andrea Giorgi2, Hervé Stevenin2, and Sara Maria Ratto2
1CIMA Foundation, Savona, Italia
2Centro Funzionale della Valle d’Aosta, Dipartimento Protezione Civile, Valle d’Aosta, Italia

Abstract. In this study, we have developed a predictive model for debris flows using machine learningtechniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorologicalvariables. The variables of the dataset were collected from daily measured and modelled data for all of thedrainage basins in which at least one debris-flow event was generated during the time period considered(2009-2019). The performances of the models obtained with different machine learning techniques wereevaluated with the ROC analysis. The most suitable model was then experimentally implemented in theexisting early warning system of the Aosta Valley Region. The model provides daily values of debris-flow
probability (DFP) for individual basins, based on the input geo-morphological and hydro-meteorologicalvariables. These results can be used to issue specific debris-flow alerts at the scale of the alert areas of theregion.

1 Introduction
Forecasting, monitoring and surveillance activities of
meteorological phenomena in the Aosta Valley region
are managed by the Centro Funzionale, with the aim ofevaluating the hydrogeological risk. The region isdivided into four alert areas, which are homogeneousin terms of meteorological conditions and soilinstabilities. The hydrogeological alerts are released
daily by a colour code (red, yellow, orange or green)
based on forecast scenarios and the severity and spatial
extent of the phenomena.
A debris-flow indicator (DFI) based on thresholdsof expected rainfall and recorded temperature waspreviously developed and implemented [1] to issue
specific debris-flow alerts caused by summer
thunderstorms, which are high-intense rainfall of shortduration. In this study, we present a new debris-flow
predictive model (DFM) obtained from machinelearning algorithms and a large dataset of several geomorphological and hydro-meteorological variables.
The data were collected for the years from 2009 to2019, in which 121 summer debris-flow eventsoccurred in 91 different drainage basins of the Aosta
Valley region. The model derived from this analysis
was then experimentally implemented in the early
warning system (EWS) of the region.

2 Debris-flow dataset
The data to develop the model were collected in thesummers from 2009 to 2019, based on the availabilityof consistent and continuous data of the hydrologicalmodel Continuum [2-3]. During this period, 121debris-flow events were recorded [4] for the total of 91
different drainage basins of the region (Figure 1).

Fig. 1. The four alert areas of the region with the weather
stations and the debris-flow basins considered in this study.

A dataset of 15 different geo-morphological andhydro-meteorological variables was built collecting
daily values of the variables for the period considered
and for each basin included in this study (Table 1).
The geo-morphological variables, which areconstant over time for each basin, were obtained from a
10m DEM and from the lithological map of the region.
The altitude was included with the mean, the maximum
and the minimum values for each basin. The slope
gradient and the orientation were obtained as theaverages of the respective values within the basin area;
the first was calculated from the tangent of the angle ofthe surface to the horizontal, whereas the latter fromthe deviation of the orientation from the North. The
lithological map was used to derive the percentage ofthe basin area covered by alluvial deposits, slopedebris, glacial deposits, landslide deposits, glacier androck.

* Corresponding author: michelponziani@gmail.com© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 07012 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341507012

the Curve (AUC), which is an indicator of the classifier
performance ranging between 0 and 1. ROC curves are
obtained plotting the relationship between the true
positive rate (TPR) and the false positive rate (FPR),
defined as follows:

Table 1. The variables of the dataset
VariableSource

Altitude [m a.s.l.]Slope gradient [°]Orientation [°]Alluvial deposits [%]Slope debris [%]Glacial deposits [%]Landslide deposits [%]Glacier [%]Rock [%]
Precipitation (Mean) [mm]Precipitation (Max) [mm]Freezing level last 10 days [°C]
Snow Cover Area [%]Soil moisture (Mean) [%]Soil moisture (Max) [%]

DEM
DEM
DEM
Lithological map
Lithological map
Lithological map
Lithological map
Lithological map
Lithological map
Rain gauges
Rain gauges
Weather stations
Hydrological model
Hydrological model
Hydrological model

TPR = TP/(TP+FN)
FPR = FP/(FP+TN)

(1)
(2)

where the true positives (TP) are the debris flows
predicted correctly, the false positives (FP) are themissed debris flows, the false negatives (FN) are thefalse alarms, and the false negatives (FN) are the nondebris flows predicted correctly.

4 Results
Figure 2 shows the ROC curves obtained applying fivemachine-learning algorithms on the testing dataset. The
best-performing model was produced by the logistic
regression, with a corresponding AUC of 0.85. All of
the resampling techniques that were then applied on thetesting dataset improved considerably the performanceof the model. The final best-performing model, which
was selected to be implemented in the EWS of theregion, was the logistic regression with ROSE
resampling, which scored an AUC of 0.95.

The hydro-meteorological variables are timedependent and hence defined with a daily temporal
resolution consistent with the information available forthe debris-flow events. The mean and the maximum
precipitation in each basin were calculated from dailyraster maps obtained with the GRISO algorithm [5]from the rain gauges distributed over the region (Figure1). The daily precipitation was chosen in this study as
it includes the short-duration thunderstorms (typically
2 to 4 hours) that trigger the summer debris flows in
the region.
The snow cover area (SCA) as well as the mean
and the maximum soil moisture content were obtained
from the hydrological model Continuum [2-3], which
was calibrated and validated for the Aosta Valley
region using hydro-meteorological, hygrometric andsatellite data. The freezing level of the last 10 days
describes the effect of persistent high temperatures [1]and it was calculated from the weather stations
distributed within the region.

3 Methods

Fig. 2. ROC curves for the five different machine-learningalgorithms applied to the testing dataset: logistic regression(LR), random forest (RF), K-nearest neighbours (KNN),
decision tree (DT), and support vector machine (SVM).

Five different machine learning techniques were used
to formulate the debris-flow predictive models: thelogistic regression model (LR), the decision tree (DT),the random forest (RF), the K-nearest neighbours(KNN), and the support vector machine (SVM).
The variable to be modelled was the debris-flow
probability, which identifies the possibility of debrisflow formation in each specific basin from 0 to 1. In
the dataset, this variable can be either 0 (no debrisflow) or 1 (debris-flow recorded) and it is unequallydistributed due to the low number of debris-flow events
recorded in the period considered. Therefore, four
resampling techniques were applied to balance thedataset: undersampling, oversampling [6], SMOTE [7]and ROSE [8].
The dataset was split into two parts: the training
dataset (80%) to develop the model, and the testingdataset (20%) to assess the model performance. The
best-performing model was determined with theReceiver Operating Characteristic (ROC) analysis [9]and the calculation of the corresponding Area Under

The variable importance for the selected model isshown in Figure 3. The precipitation and the mean
freezing level are the variables that weight the most in
the debris-flow prediction, followed by the snow coverarea of the basin. This study only included specificdebris-flow basins, therefore geo-morphologicalvariables such as the slope gradient were found to beless important by the prediction algorithms.
The application of the model on the testing dataset
showed that the debris-flow events of the past werepredicted with a mean probability of 77%, whereas thedays with no debris flows had a mean probability of25%.

2

E3S Web of Conferences 415, 07012 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341507012

values. The overall best-performing model was
selected to be experimentally implemented in the EWSof the Aosta Valley region as an improvement of theexisting debris-flow indicator (Ponziani et al. 2020).
Compared to conventional rainfall thresholds, themodel developed with machine-learning algorithms
includes a large number of variables that have animpact on the resulting debris-flow probability. The
most important variables of the model were found to be
the precipitation, the freezing level of the last ten days
and the snow cover area. This confirms that intense
rainfalls and prolonged high temperatures increase theprobability of debris flows triggered by thunderstorms
in the Alpine region of Aosta Valley, while thepresence of snowpack considerably reduces the debrisflow probability.
The debris-flow model was implemented in the EWSof the Aosta Valley region to provide a daily debrisflow probability for the 91 selected basins. The input
daily variables are obtained from the weather forecast
bulletin (precipitation), the hydrological model (snowcover area and soil moisture content) and the weather
stations (mean freezing level of the previous ten days).
However, even though the debris-flow probability iscomputed for 91 basins, the results can be operatively
used at the moment only at the scale of the alert area.

Fig. 3. The variable importance for the selected model

5 Operational Use
The selected best-performing model for debris-flow
predictions was experimentally implemented in thecurrent EWS of the Aosta Valley region. The data
feeding the model are the geomorphologicalcharacteristics of each basin, the precipitation takenfrom the weather forecast bulletin released by theRegional Meteorological Office, the soil moisture
content and the snow cover area obtained from thehydrological model, and the freezing level of the lastten days derived from the weather stations. The outputof the model is a debris-flow probability (DFP) for
each basin that is set to zero if the forecast freezing
level is lower than the mean altitude of the basin (i.e.
expected snowfall on the majority of the basin area) or
if there is no specific thunderstorm warning in theweather forecast bulletin. This is because the model
was specifically set to predict debris flows caused byshort intense rainfalls like thunderstorms.
In the alert bulletin released daily by the Functional
Centre, the hydrogeological alerts caused byinstabilities, such as landslides, debris flows, and rockfalls, are issued for each of the four alert areas (Figure1). Moreover, the forecast precipitation is available
only at the spatial resolution of the alert area and,consequently, all of the basins in the same alert area
have the same input precipitation. For these reasons, noalert can be released for any individual basin and thecriteria to issue a debris-flow alert for an entire alertarea is based on the scenario of three or more expected
debris flows (Ponziani et al. 2020). In the experimental
phase, debris-flow alerts are issued when three or morebasins exceed the DFP threshold (set to 0.80) in thesame alert area.

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6 Conclusions
For this study, we have built a debris-flow dataset for
91 different drainage basins in the Aosta Valley region,
including several geo-morphological features and dailyhydro-meteorological variables for each of the basins
for the summers from 2009 to 2019. The dataset wasused to develop a debris-flow predictive model using
five different machine learning methods and four
resampling techniques. The results of the analysis werethen assessed with the ROC curves and the AUC

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