Combining modelled snowpack stability with machine learning to predict avalanche activity
Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of...
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2023
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Online Access: | https://doi.org/10.5194/tc-17-2245-2023 https://doaj.org/article/497b8897b7744b58b96669a7d5edab07 |
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ftdoajarticles:oai:doaj.org/article:497b8897b7744b58b96669a7d5edab07 2023-07-02T03:33:51+02:00 Combining modelled snowpack stability with machine learning to predict avalanche activity L. Viallon-Galinier P. Hagenmuller N. Eckert 2023-06-01T00:00:00Z https://doi.org/10.5194/tc-17-2245-2023 https://doaj.org/article/497b8897b7744b58b96669a7d5edab07 EN eng Copernicus Publications https://tc.copernicus.org/articles/17/2245/2023/tc-17-2245-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-2245-2023 1994-0416 1994-0424 https://doaj.org/article/497b8897b7744b58b96669a7d5edab07 The Cryosphere, Vol 17, Pp 2245-2260 (2023) Environmental sciences GE1-350 Geology QE1-996.5 article 2023 ftdoajarticles https://doi.org/10.5194/tc-17-2245-2023 2023-06-11T00:37:07Z Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting. Article in Journal/Newspaper The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 17 6 2245 2260 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 L. Viallon-Galinier P. Hagenmuller N. Eckert Combining modelled snowpack stability with machine learning to predict avalanche activity |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting. |
format |
Article in Journal/Newspaper |
author |
L. Viallon-Galinier P. Hagenmuller N. Eckert |
author_facet |
L. Viallon-Galinier P. Hagenmuller N. Eckert |
author_sort |
L. Viallon-Galinier |
title |
Combining modelled snowpack stability with machine learning to predict avalanche activity |
title_short |
Combining modelled snowpack stability with machine learning to predict avalanche activity |
title_full |
Combining modelled snowpack stability with machine learning to predict avalanche activity |
title_fullStr |
Combining modelled snowpack stability with machine learning to predict avalanche activity |
title_full_unstemmed |
Combining modelled snowpack stability with machine learning to predict avalanche activity |
title_sort |
combining modelled snowpack stability with machine learning to predict avalanche activity |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-2245-2023 https://doaj.org/article/497b8897b7744b58b96669a7d5edab07 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 17, Pp 2245-2260 (2023) |
op_relation |
https://tc.copernicus.org/articles/17/2245/2023/tc-17-2245-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-2245-2023 1994-0416 1994-0424 https://doaj.org/article/497b8897b7744b58b96669a7d5edab07 |
op_doi |
https://doi.org/10.5194/tc-17-2245-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
6 |
container_start_page |
2245 |
op_container_end_page |
2260 |
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1770273962223206400 |