A regional early warning system for debris flows

In this study, we have developed a predictive model for debris flows using machine learning techniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the...

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Main Authors: Ponziani Michel, Ponziani Denise, Giorgi Andrea, Stevenin Hervé, Ratto Sara Maria
Format: Article
Language:English
Published: EDP Sciences 2023-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/52/e3sconf_dfhm82023_07012.pdf
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author Ponziani Michel
Ponziani Denise
Giorgi Andrea
Stevenin Hervé
Ratto Sara Maria
author_facet Ponziani Michel
Ponziani Denise
Giorgi Andrea
Stevenin Hervé
Ratto Sara Maria
author_sort Ponziani Michel
collection DOAJ
description In this study, we have developed a predictive model for debris flows using machine learning techniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the drainage 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 were evaluated with the ROC analysis. The most suitable model was then experimentally implemented in the existing 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-meteorological variables. These results can be used to issue specific debris-flow alerts at the scale of the alert areas of the region.
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spelling doaj.art-1a8e42602553492b82e2daa1e601530e2023-08-21T09:02:38ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014150701210.1051/e3sconf/202341507012e3sconf_dfhm82023_07012A regional early warning system for debris flowsPonziani Michel0Ponziani Denise1Giorgi Andrea2Stevenin Hervé3Ratto Sara Maria4CIMA FoundationCIMA FoundationCentro Funzionale della Valle d’Aosta, Dipartimento Protezione CivileCentro Funzionale della Valle d’Aosta, Dipartimento Protezione CivileCentro Funzionale della Valle d’Aosta, Dipartimento Protezione CivileIn this study, we have developed a predictive model for debris flows using machine learning techniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the drainage 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 were evaluated with the ROC analysis. The most suitable model was then experimentally implemented in the existing 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-meteorological variables. These results can be used to issue specific debris-flow alerts at the scale of the alert areas of the region.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/52/e3sconf_dfhm82023_07012.pdf
spellingShingle Ponziani Michel
Ponziani Denise
Giorgi Andrea
Stevenin Hervé
Ratto Sara Maria
A regional early warning system for debris flows
E3S Web of Conferences
title A regional early warning system for debris flows
title_full A regional early warning system for debris flows
title_fullStr A regional early warning system for debris flows
title_full_unstemmed A regional early warning system for debris flows
title_short A regional early warning system for debris flows
title_sort regional early warning system for debris flows
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/52/e3sconf_dfhm82023_07012.pdf
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