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Simplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area / David Marín-García in Sustainable Cities and Society, vol 88 (January 2023)  

Public [article]  inSustainable Cities and Society > vol 88 (January 2023) . - n° 104251 Titre : Simplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area Type de document : Article/Communication Auteurs : David Marín-García, Auteur ; Juan Rubio-Gómez-Torga, Auteur ; Manuel Duarte-Pinheiro, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104251 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] acquisition de données [Termes IGN] Andalousie [Termes IGN] apprentissage automatique [Termes IGN] bassin hydrographique [Termes IGN] bâtiment [Termes IGN] cartographie des risques [Termes IGN] coefficient de corrélation [Termes IGN] dommage matériel [Termes IGN] évaluation des paramètres [Termes IGN] image à haute résolution [Termes IGN] modèle de simulation [Termes IGN] zone inondable Résumé : (auteur) Flooding due to overflowing rivers affects the construction elements of many buildings. Although significant progress has been made in predicting this damage, there is still a need to continue studying this issue. For this reason, the main goal of this research focuses on finding out if, based on a small dataset of cases of a given area, it is possible to predict at least three degrees of affectation in buildings, considering only three environmental factors (minimum distance from the river, unevenness and possible water communication). To meet this goal, the methodological approach followed considers scientific literature review and collection and analysis of a small dataset from 101 buildings that have been affected by floods in the Guadalquivir River basin (Andalusia. Spain). After analyzing this data, algorithms based on machine learning (ML) are applied to predict the degree of affection. The results, analysis and conclusions indicate that, if the study focuses on a specific area and similar buildings, using a correlation matrix and ML algorithms such as the "Decision Tree" with cross-validation, around 90% can be achieved in the "Recall" and "Precision" of "High-Level-Affection" class, and an “Accuracy” around 80% in general. Numéro de notice : A2023-006 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2022.104251 Date de publication en ligne : 15/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104251 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102093 [article]