Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach

Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning (ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual...

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Veröffentlicht in:	Journal of geographical sciences 2024-08, Vol.34 (8), p.1513-1536
Hauptverfasser:	Yang, Huilin, Yao, Rui, Dong, Linyao, Sun, Peng, Zhang, Qiang, Wei, Yongqiang, Sun, Shao, Aghakouchak, Amir
Format:	Artikel
Sprache:	eng
Schlagworte:	Agricultural land Earth and Environmental Science Environmental risk Flood forecasting Floods Geographical Information Systems/Cartography Geography Machine learning Nature Conservation Physical Geography Precipitation Remote Sensing/Photogrammetry Risk management
Online-Zugang:	Volltext
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