Data-driven flood susceptibility assessment using hybrid machine learning and optimization techniques: case of the Sedrata Watershed, NE Algeria

Abstract Flooding is one of the most disastrous natural hazards around the globe, causing enormous ecological and socio-economic losses; therefore, reliable assessment tools are required for informed risk management. This research proposes a hybrid flood susceptibility modeling framework that incorp...

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Autori principali:	Elhadi Mechentel, Sabri Dairi, Abdelouahab Lefkir, Saeid Eslamian, Habib Abida, Yassine Djebbar
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2026-04-01
Serie:	Scientific Reports
Soggetti:	Flood susceptibility mapping Machine learning Metaheuristic optimization Satellite imagery Sedrata Watershed
Accesso online:	https://doi.org/10.1038/s41598-026-43262-9
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