Integrating geospatial intelligence and machine learning for flood susceptibility mapping

Abstract Flood susceptibility mapping using machine learning models and remote sensing datasets has emerged as an effective approach for identifying flood-prone areas. The main objective of this study was to evaluate flood susceptibility using five ML algorithms: Extreme Gradient Boosting (XGBoost),...

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Autores principales:	Mehdi Rahimi, Bahram Malekmohammadi, Mohammad Karimi Firozjaei, Reza Kerachian, Jamal Jokar Arsanjani, Mou Leong Tan, Joseph Awange, Dragan Savic, Qingyun Duan, Amir AghaKouchak
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Nature Portfolio 2026-02-01
Colección:	Scientific Reports
Materias:	Flood mapping Susceptibility Machine learning Geospatial data analysis Mapping
Acceso en línea:	https://doi.org/10.1038/s41598-026-41014-3
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