| Autor: |
Ponziani Michel, Ponziani Denise, Giorgi Andrea, Stevenin Hervé, Ratto Sara Maria |
| Jazyk: |
English<br />French |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
E3S Web of Conferences, Vol 415, p 07012 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202341507012 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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