# MACHINE LEARNING FOR AVALANCHE ACTIVITY FORECASTING: COMBINING THE KNOWLEDGE OF PAST AVALANCHE ACTIVITY AND SNOW MODELLING ANALYSIS

VIALLON-GALINIER Léo¹*, POUSSEREAU Marianne¹, HAGENMULLER Pascal¹, LAFAYSSE Matthieu¹

¹Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France

**ABSTRACT:** Predicting avalanche activity is critical in mountainous areas to support early warning services. For this purpose, and to complement field observations, snow cover modelling provides information otherwise unavailable on the present and future state of the snowpack. We gather observations of avalanche activity, mainly from ski resorts, and use machine learning to relate the observations and modelled snow conditions. The model brings information on snowpack evolution along time, with some specific attention paid to indicators dedicated for avalanche activity while observations provide the feedback from the terrain. The relation between modelled snow conditions and avalanche activity is optimized by the machine learning model to provide relevant information on avalanche activity. We provide estimations of the avalanche activity based on locally observed avalanches but also use the relation between modelled snow conditions and avalanche activity to transfer information across space. The model could be trained in data-rich regions and then applied on areas with less information available on past avalanche activity as we try to be as close as possible to the relation between snow conditions and avalanche activity. The results were evaluated against different dataset available in France: both data from ski resorts providing daily observations of avalanche activity and avalanches recorded in pre-defined avalanche paths by forest rangers (EPA from ONF - INRAE). Such method allow to transfer the information from data-rich regions to data-poor regions and thus improve globally the knowledge of avalanche conditions. More generally, it opens perspectives to provide new tools to forecasters in charge of the redaction of avalanche bulletins.

**Keywords:** Avalanche, Machine learning, Snow modelling

## 1. INTRODUCTION

Avalanches cause around 30 fatalities each year in France and around 100 in Europe, mainly winter recreationists in backcountry areas. To reduce the risk associated to avalanche hazard, training and daily information on the avalanche hazard are the two pillars many countries have set up. Daily information is provided in avalanche bulletins widely broadcast to the general public. To produce these bulletins, avalanche forecasters need to have a precise overview of the snowpack across mountain ranges. For that purpose, the two mains sources of information are observations and snow modelling. Observations are critical as they provide accurate information on local snow conditions. However, the French mountains (Alps, Pyrenees and Corsica) covers around 33,000 square kilometers and it is thus not an option to get detailed observations of the snowpack, including its layering, critical to understand avalanche formation (Schweizer et al., 2016), on such large areas. Models are complementary of the observations as they are able to provide relevant information for avalanche forecasters on areas no covered by observations.

Snow models provide large quantities of observations which cannot be directly used as a whole by forecasters due to the too important quantity of data provided. Avalanche-oriented tools are thus needed for an efficient use of the detailed information provided by snow models (Horton et al., 2020). Models constituted of sets of expert rules (Giraud et al., 2002) or identification of past similar observed situations to infer expected avalanche hazard (Navarre et al., 1987; Buser, 1989) were first developed to help interpreting models or large observation datasets. However, these approaches were limited to observations or suffer from a lot of noise, making their use not much effective in practical forecasting. Recent work using Random Forest machine learning method have shown to be relevant for avalanche-centered estimators from snow cover models (Sielenou et al., 2021; Pérez-Guillén et al., 2022; Mayer et al., 2022).

The aim of this study, similarly to previously presented tools, is to facilitate the use of snow modelling by avalanche forecasters. We chose to focus on two major elements for the analysis of avalanche forecasters which is the probability of avalanche occurrence and spatial localization of this avalanche activity (Statham et al., 2018). To this end, we develop a machine learning based method to infer avalanche activity from snow model outputs, at the spatial resolution of the snow cover model, and calibrated with past avalanche observations. We used

<footnote>*Corresponding author address:
Léo Viallon-Galinier
Centre d'Études de la Neige
38100 Grenoble - France;
tel: ++33 476637903;
email: leo.viallon@meteo.fr</footnote>
state of the art stability indices to reduce the information of snow models with physical knowledge to improve machine learning performances (Viallon-Galinier et al., 2022). We also used a new dataset of avalanche observation for training (Giard et al., 2018).

This paper details the new machine learning model built on top of a new observation dataset to provide an estimation of avalanche activity provided modelled snow and meteorological conditions. We evaluate the performances of this machine learning model across two different avalanche datasets available in France. Then, we estimate the possibility to spatially extend the method and show that the learned relation between snow and meteorological conditions and avalanche activity is relevant and can be used on areas with less or even no observation data available for training.

## 2. MATERIAL AND METHOD

### 2.1 Study area and time period

We focus, in this study, on a selection of forecasting areas, namely the Haute-Maurienne, Haute-Tarentaise and Vanoise in the northern French Alps and Haute-Bigorre in the French Pyrenees. These areas where chosen to compare similar areas with similar climates (e.g. Haute-Maurienne and Haute-Tarentaise), areas that are geographically close but with more climate differences (e.g.Haute-Maurienne and Vanoise) and areas with completely different climates (e.g. Haute-Maurienne and Haute-Bigorre) (Reuter et al., 2022). The selected forecasting areas are presented on Figure 1.

<figure><img src="image_1.png" /><figcaption>Figure 1: Map of selected forecasting areas from French Alps and Pyrenees: Haute-Maurienne, Haute-Tarentaise, Vanoise and Haute-Bigorre.</figcaption></figure>

We used for both training and evaluation all data available in the period 1970-2020.

### 2.2 Snow modelling

We use the SAFRAN-SURFEX/Crocus model (Brun et al., 1989; Vionnet et al., 2012) to simulate the meteorological and snow conditions in the selected forecasting areas. We select the 8 aspects and three elevation bands centered at 1800, 2400 and 3000m on 40 degree slopes. We use the publicly available snow and meteorological reanalysis fully described in Vernay et al. (2022).

The Crocus model provide the detailed layering of the snowpack for each point in time and space. We compute then avalanche-oriented stability indices, as described in Viallon-Galinier et al. (2022), mainly shear strength-stress ratio with and without the presence of a skier at the surface and critical crack length in order to reduce the large amount of data produced by snow model, with avalanche-oriented indices.

As the time dimension is crucial for avalanche release (Conway and Wilbour, 1999), time-derivatives of meteorological, snow variables and stability indices are also computed (see Viallon-Galinier et al., 2023).

### 2.3 Avalanche datasets

In France, two main avalanche observation datasets are currently available and used in this study. The first one consists of observations from ski resorts that report the daily number of observed avalanches as well as the elevation and aspect range of observed avalanches (Giard et al., 2018). This first dataset is usually referred to as the snow-meteorological network and hereafter abbreviated SMN. Data are available only during the opening period of ski resorts, that is to say approximately between December 20 and late April. Moreover, this observation network started in the seventies and we here consider data starting in 1994 to have a homogeneous dataset.

The second dataset is used mainly for evaluation and is the identification of all avalanches reaching an observation elevation threshold in around 3,000 pre-identified avalanche paths in the French Alps and Pyrenees. The observation period usually covers from November 15 to May 15 (Sieleinou et al., 2021). We focus on the period 1970-2020. This dataset focuses on large avalanches that comes close to infrastructures, dwellings or at least is not limited to high altitude and goes down to the valley. A full presentation of the dataset and pre-processing is available in Viallon-Galinier et al. (2023). This dataset is called the Enquête Permanente Avalanche or EPA (ONF/INRAE) (Bourova et al., 2016).

Information from both datasets are synthesized to get mean number of observed avalanches, in each forecasting area, across the same aspect and elevation bands as selected for the snow modelling.
## 2.4 Machine learning model

We use a Random Forest (Breiman, 2001), which are a set of independent and automatically optimized decision trees. The model uses as input the snow and meteorological conditions from Crocus snow cover model as well as stability indices and derivatives previously presented. The model is trained with data from the SMN dataset by relating observed avalanche activity with the snow and meteorological conditions modelled in the same aspect and elevation band. Detailed implementation (except for the training dataset) is available in Viallon-Galinier et al. (2023).

An observed avalanche situation is defined as a situation where at least one avalanche is reported in the avalanche dataset in the area of interest (forecasting area, elevation and aspect band). The Random Forest is trained on the binary information: observed avalanche situation or non-avalanche situation. The trained model then provides as an output a probability of being an avalanche situation (or day) for each provided meteorological and snow condition.

## 2.5 Evaluation

To evaluate the performances of the machine learning model, we compare days of avalanche activity between the machine learning model and the different available data sources. We ensure that no data of the same dataset and the same year is used between training and evaluation to ensure independence of the evaluation. Indeed, seasonal snow cover keep the history of past meteorological conditions, and an evaluation that would not address this specificity of snow science would not be fair: when the previous days or neighbour elevation/aspects are partitioned between training an devaluation sets, the scores could not be comparable with operational forecasting capabilities.

The output of machine learning is a probability of being in an avalanche prone situation. We can select a threshold between 0 and 1 on the output probability of the Random Forest to separate between avalanche and non-avalanche prone situations. Afterward, we use classical tools of binary classification evaluation. We compute the confusion matrix and classical scores: true positive rate (TPR), false positive rate (FPR), balanced accuracy (BA) and negative predictive value (NPV). True positive rate represent the part of observed avalanches that are correctly predicted. False positive rate is the part of non-avalanches situations that are misclassified. Balanced accuracy combines both by TPR and FPR by averaging TPR and 1-FPR. Negative predictive value compares the correctly predicted non avalanche situations to all predicted non-avalanche situations. It is complementary to TPR

and FPR as it provides information on underestimation of avalanche activity.

An overall evaluation is conducted through the usual ROC (Receiver Operating Characteristic) curve that allow to select the optimal threshold for a given avalanche activity to forecast. The optimal threshold is optimized for each forecasting area independently. The ROC curve represents TPR as a function of FPR for all possible thresholds. An overall estimation of the performances of a classifier could be provided by the area under ROC curve or AUC. We provide hereafter both the ROC curves and corresponding AUC for quantitative comparisons. We also provide scores for the optimal threshold.

Evaluation is conducted at the scale of each aspect and elevation band, which are then combined in the confusion matrix. This way, we both evaluate the avalanche activity itself and the location of the predicted avalanche activity. We focus on winter period and also remove situations where there is no snow on ground (minimal modelled snow on ground set to 10cm). The winter period is limited by the observation period of each datasets: December 20 to late April between 1994 and 2020 for SMN and November 15 to May 15 from 1970 to 2020 for EPA.

## 2.6 Local tuning and spatial transfer

By default, the machine learning model is trained and evaluated on the same forecasting area. However, the machine learning model is trained in order to relate snow and meteorological conditions to avalanche activity. Hence, once trained, it could be applied wherever snow and meteorological conditions are modelled with the assumption that the underlying physical processes are the same and the selected variables contain enough information to describe the snow and meteorological conditions. We thus also train the model on one forecasting area (Haute-Maurienne) and apply the model to two others forecasting areas: Haute-Tarentaise and Haute-Bigorre and evaluate the performances of the model. This latter approach allows to estimate the transferability of the method to areas with less or even no observation data available for training.

# 3. RESULTS

## 3.1 A practical example

An example of the output of the machine learning model on Haute-Maurienne area, at 2400m and northern aspect is provided on Figure 2. The output of the machine learning model is compared to the observed avalanche activity in the same area, altitude and aspect range. The comparison shows an overall good agreement between high probability and observations but also many situations in which the probability of being in an avalanche situation is comparatively high and no avalanches are observed in the independent EPA dataset.
<figure><img src="image_2.png" /><figcaption>Figure 2: Machine learning model output for Haute-Maurienne area, 2400m elevation, northern aspect and winter 2018-2019. Gray bars represent the observed avalanches in the EPA dataset for northern aspect (from north-east to north-west).</figcaption></figure>

<table><caption>Table 1: Confusion matrix for the machine learning model trained and evaluated on Haute-Maurienne, for all aspects and elevations, compared to observations of SMN dataset: number of situations (day, aspect and elevation band) in which avalanche are observed depending on the situation identified by the machine learning model (non-avalanche or avalanche-prone situation).</caption><thead><tr><th rowspan="2">Observation</th><th colspan="2">Prediction</th></tr><tr><th>Non-Avalanche</th><th>Avalanche</th></tr></thead><tbody><tr><td>Non-Avalanche</td><td>41 078</td><td>14 395</td></tr><tr><td>Avalanche</td><td>1 181</td><td>3 356</td></tr></tbody></table>

## 3.2 Systematic evaluation

To go beyond examples and provide a more systematic evaluation, we first provide the confusion matrix to compare predicted avalanche-prone situations to observed avalanche situations in the SMN dataset (Table 1). Most of the situations are on the diagonal, meaning that most of the situations are correctly identified. We also have 14,395 days, aspects and elevations on the whole period (1994-2020) for which the model identify an avalanche-prone situation but no avalanche were reported in the available dataset. However, in few situations (1,181) avalanches are reported but the model was not able to identify the situation as an avalanche-prone situation.

We also provide in Figure 3 the ROC curves for the machine learning model trained in Haute-Maurienne and evaluated through the two available datasets of EPA and SMN for all aspects and elevations considered. The ROC curves are above the 1:1 line that corresponds to a random classifier and thus show a relevant prediction on average. More quantitatively, we obtain an AUC of 0.81 with SMN dataset and 0.75 with EPA dataset. We provide in Table 2 the scores of the model for the two evaluation datasets. The TPR is higher for SMN dataset compared to EPA, with respectively 74.0 and 68.2%. The FPR is higher for EPA dataset compared to SMN dataset with respectively 29.1 and 26.0%. The balanced accuracy is then higher with SMN dataset with 74.0% compared to 69.6% for EPA. The NPV is very high with 99.5% for EPA and 97.2% for SMN meaning that nearly no avalanche situation is missed.

<figure><img src="image_3.png" /><figcaption>Figure 3: ROC curve of the Machine learning model output for Haute-Maurienne area, all elevations and aspects for evaluation with both EPA (blue curve) and SMN (orange curve).</figcaption></figure>

<table><caption>Table 2: Scores on Haute-Maurienne area, for all aspects and elevations regarding the two considered datasets: EPA and SMN.</caption><thead><tr><th>Scores</th><th>SMN</th><th>EPA</th></tr></thead><tbody><tr><td>AUC</td><td>0.81</td><td>0.75</td></tr><tr><td>TPR</td><td>74.0 %</td><td>68.2 %</td></tr><tr><td>FPR</td><td>26.0 %</td><td>29.1 %</td></tr><tr><td>BA</td><td>74.0 %</td><td>69.6 %</td></tr><tr><td>NPV</td><td>97.2 %</td><td>99.5 %</td></tr></tbody></table>

## 3.3 Application to other areas

To go beyond the test area of Haute-Maurienne we also applied the same method on two other areas to ensure that the performances are transferable to other areas. The training is done on each forecasting area. Table 3 present the results for the different areas: Haute-Maurienne, Haute-Tarentaise, Vanoise and Haute-Bigorre. Results are similar for all considered massifs with TPR between 74.0 and 68.5%, FPR between 26 and 32.3% and always a very high NPV so nearly no avalanches missed. The Haute-Maurienne may be the most different from the others with a slightly higher TPR but also a higher FPR.
<table><caption>Table 3: Scores on different areas (Haute-Maurienne; Haute-Tarentaise, Vanoise and Haute-Bigorre), for all aspects and elevations regarding SMN dataset. For each area, the model is trained on independent data corresponding to the studied area.</caption><thead><tr><th>Scores</th><th>H-Maurienne</th><th>H-Tarentaise</th><th>Vanoise</th><th>H-Bigorre</th></tr></thead><tbody><tr><td>AUC</td><td>0.81</td><td>0.77</td><td>0.76</td><td>0.75</td></tr><tr><td>TPR</td><td>74.0 %</td><td>72.2 %</td><td>69.6 %</td><td>68.5 %</td></tr><tr><td>FPR</td><td>26.0 %</td><td>32.3 %</td><td>32.1 %</td><td>31.3 %</td></tr><tr><td>BA</td><td>74.0 %</td><td>70.0 %</td><td>68.8 %</td><td>68.6 %</td></tr><tr><td>NPV</td><td>97.2 %</td><td>96.3 %</td><td>95.9 %</td><td>95.0 %</td></tr></tbody></table>

<table><caption>Table 4: Scores of the model trained on Haute-Maurienne and applied on Vanoise and Haute-Bigorre, for all aspects and elevations regarding SMN dataset.</caption><thead><tr><th>Scores</th><th>Vanoise</th><th>Haute-Bigorre</th></tr></thead><tbody><tr><td>AUC</td><td>0.75</td><td>0.70</td></tr><tr><td>TPR</td><td>68.8 %</td><td>66.6 %</td></tr><tr><td>FPR</td><td>30.1 %</td><td>35.9 %</td></tr><tr><td>BA</td><td>69.3 %</td><td>65.3 %</td></tr><tr><td>NPV</td><td>95.9 %</td><td>94.4 %</td></tr></tbody></table>

## 3.4 Spatial transfer

We trained a machine learning model on Haute-Maurienne and then applied the model to Vanoise and Haute-Bigorre to estimate the possibility to transfer a trained model to other areas where less or even no data is available. The performances of the model on SMN dataset on Vanoise and Haute-Bigorre are assessed in Table 4. The performances are for both areas of the similar order of a few points lower only compared to the results of the machine learning model trained on local data. The AUC is 0.75 compared to 0.76 for Vanoise and 0.70 versus 0.75 for Haute-Bigorre. Haute-Bigorre have a larger performance decrease compared to locally trained model relatively to Vanoise with a decrease of 5 points for balanced accuracy for Haute-Maurienne whereas Vanoise have a decrease of around 3 points. The NPV is still very high and less than one point lower compared to the locally trained model.

## 4. DISCUSSION

In this study, we combined the knowledge of past avalanche activity and snow modelling analysis using machine learning to provide an estimation of expected avalanche activity. We used a new dataset from snow and meteorological observation network available in France. This new dataset get rid of many limitations of previously used dataset as EPA (Viallon-Galinier et al., 2022), such as the limitation to large avalanches reaching low altitudes and uncertainties in the observation dataset on the date of events as we take advantage of the observation from ski resorts employees, crossing and securing the skiing domain daily.

We showed that the trained machine learning model display a good qualitative agreement with avalanche

observations, and quantitatively evaluate the performances of the model on two independent avalanche datasets available in France: the SMN also used for training and EPA. The first results on Haute-Maurienne forecasting area are then extended to different areas, from the Northern Alps to the French Pyrenees, by tuning models on each area. Finally, we estimated the ability to spatially transfer a trained model to other areas, which is of crucial interest for areas with fewer observations available.

All presented global evaluations are conducted on all aspects and elevation, so we both evaluate that we are able to estimate correctly the observed avalanche activity in time but also that we correctly identified the areas where the avalanche activity was observed (elevation band and aspect). The model thus not only provide an estimation of the expected avalanche activity but can also be used to provide information on spatial repartition of avalanche hazard, which are two main points needed for the redaction of avalanche bulletins (Statham et al., 2018).

### 4.1 Robustness of the method

Once trained, the machine learning model present significant variations of the output probability between days with no avalanche activity and days identified as more prone to avalanche activity. These days prone to avalanche activity are shown to be well correlated to observed avalanche activity (Figure 2). For high output probability and no observations, due to the scarcity of avalanche release and limits of the observation network, it is possible that avalanches occurred but were not observed or that the snowpack had poor stability but no avalanche released. Besides, days for which model output probability is low but avalanches were observed are quite rare, which is relevant for a practical use of the model. This behavior is confirmed with the systematic evaluation (Table 1 and 2).

We also ensure that the results are not specific to one dataset. We evaluate the model trained on SMN dataset against the two available datasets: SMN and EPA. The avalanche activity observed by the two dataset are quite different as SMN record al avalanches that can be observed from observation point located at high elevation whereas EPA focus on large avalanches that come close to infrastructures and observed mainly from the valleys. Thus, fewer avalanches are observed in the EPA dataset and false alarm are more frequent as avalanches may have occurred but they may not have reached the observations thresholds of EPA. However, scores and performances are not so different (Figure 3 and Table 2). We thus ensure that the method is robust enough and not specific to the training dataset and may be used for a wide variety of questions around avalanche activity.
Absolute scores are always complex to compare to literature as they are highly influenced by the dataset and the method used. For instance, if point estimations near meteorological stations are common (e.g. Pérez-Guillén et al., 2022), or local estimations regarding stability measurements (e.g. Mayer et al., 2022), evaluations at such spatio-temporal resolutions of the avalanche activity are rare (e.g. Viallon-Galinier et al., 2022). In our case, an avalanche activity correctly predicted should be predicted at correct time and location (in terms of elevation and aspect) and avalanche activity predicted in neighbour spatio-temporal area (just before or after or in neighbour aspect or elevation) will be counted as a miss for the observed avalanche time, aspect and elevation and a false alarm in the area where the avalanche was predicted. However, our performances are in the same order of magnitude as for previous studies, with 72 to 88% of accuracy (Pérez-Guillén et al., 2022; Mayer et al., 2022).

## 4.2 Spatial transfer

In some areas, because they concentrate ski resorts and mountain infrastructures, the avalanche activity is well known and recorded in databases. However, some more remote areas or more recently equipped areas may not benefit from such databases whereas forecasters also have to provide information on these areas. In this work, we try to relate snow and meteorological conditions to avalanche activity, to be further able to transfer a model trained on data-rich regions to a data-poor region. However, random forest provides correlations that may not be linked to underlying physics and may be limited to specific areas. We thus evaluate the potential for model transfer to other areas by training the model on a data-rich area (Haute-Maurienne) and applying on two different areas where sufficient data is available for evaluation. We used Vanoise, also in Northern Alps, with similar overall climate (Reuter et al., 2022) even though main precipitation events occur with oceanic perturbations while Haute-Maurienne is more impacted from snow coming from Italy. We also use Haute-Bigorre in the French Pyrenees, with a very different climate. Even though the performances are slightly lower on areas where the model was not specifically trained, we do not observe collapse of the scores (Table 4). The reduction of performances is more pronounced for Haute-Bigorre, with a different climate compared to Vanoise that is close to Haute-Maurienne massif where the model was calibrated. These results give confidence in the possibility to apply such method on data-poor regions by using our knowledge of avalanche activity in data-poor region by modelling the snow and meteorological conditions in remote areas and applying avalanche

models trained with data in surrounding areas, especially when snow conditions are similar.

## 4.3 Main advantages and limits of the method

The main advantages of the method is to treat simultaneously all avalanche activity, i.e. dry-snow, wet snow and mixed-type avalanches and to provide information on both the avalanche activity and the spatial repartition. Inherently, it comes with its drawbacks. There is no information on the avalanche type, and a complementary model should be combined to obtain this information, such as Reuter et al. (2021). The goal of the model is to synthesize point information of the snow model into an avalanche-oriented diagnosis but not to synthesize the information at the scale of the avalanche bulletin, which is an other objective addressed by other models (Pérez-Guillén et al., 2022).

We present here results on binary classification between situations prone or not to avalanche. The threshold chosen here for illustration of at least one avalanche on average observed to be in avalanche situation could be changed for instance for focusing on large avalanche events. Moreover, we are here limited to an avalanche activity and spatial repartition. For the analysis of avalanche hazard, other information is required, such as avalanche size or avalanche problem type (Statham et al., 2018).

## 5. CONCLUSION AND OUTLOOKS

This paper combines knowledge of past avalanche activity with a new dataset and snow modelling to estimate expected activity. A trained machine learning model is shown to be well correlated to observed avalanche activity on two independent datasets, highlighting the robustness of the method and the results that are not limited to the reproduction of a specific observation dataset. The extension to different forecasting areas also show that the results can be spatially transferred to other areas, with different climate, from Northern French Alps to French Pyrenees. We also estimate the possibility to transfer models trained in data-rich regions and apply it in data-poor region by applying a model trained in one forecasting areas in another one. The first results indicate that it could be relevant to use neighbour areas with more information to transfer learning from data-rich regions to data-poor regions, especially with similar avalanche climates. This model could thus be a relevant tool to guide avalanche forecasters in the large data lake produced by snow models, especially in a context of increase of the resolution of such models (Lehning et al., 2006; Lafaysse, 2023; Baron et al., 2024). It could be an avalanche-oriented tool to help avalanche forecasters in the analysis of avalanche hazard. It should be used in combination with other complementary tools and
data: the goal of this method is to provide information of avalanche activity and location whereas other information is required for avalanche bulletins.
We here compare results with two independent avalanche observation datasets, namely the SMN and EPA. New data sources, for instance with spatial observation, are also developing (Karas et al., 2022). Rather than using independently the different datasets, it would be meaningful to build a reference dataset by combining the different information sources to get together the most large and precise data on avalanche-related questions. The same way, we focus here on some elements of avalanche hazard. The next step may be to look for the better combination and synthesis of data to inform efficiently avalanche forecasters, recreationists and authorities.

## ACKNOWLEDGEMENT

We thank ONF and INRAE for the collection and maintenance of the EPA dataset and all the members of the snow and meteorological network organized by Meteo-France as well as the avalanche forecasters that take care of the quality of the data in this latter dataset by their daily review of the data collected. We also thank EDF (Électricité de France) that funded an internship that allow to work on this data. Last but not least, we thank Marie Dumont for her advices in the redaction of this paper.

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