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Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)  

Public [article]  inNatural Hazards > vol 106 n° 1 (March 2021) . - pp119 - 137 Titre : Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB Type de document : Article/Communication Auteurs : Mahya Norallahi, Auteur ; Hesam Seyed Kaboli, Auteur Année de publication : 2021 Article en page(s) : pp119 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme génétique [Termes IGN] apprentissage automatique [Termes IGN] cartographie des risques [Termes IGN] classification bayesienne [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] entropie maximale [Termes IGN] inondation [Termes IGN] Iran [Termes IGN] zone urbaine Résumé : (auteur) Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. Numéro de notice : A2021-418 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04453-3 Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04453-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97768 [article]