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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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Principais autores: Elhadi Mechentel, Sabri Dairi, Abdelouahab Lefkir, Saeid Eslamian, Habib Abida, Yassine Djebbar
Format: Artigo
Sprog:Inglês
Udgivet: Nature Portfolio 2026-04-01
Serier:Scientific Reports
Fag:
Flood susceptibility mapping
Machine learning
Metaheuristic optimization
Satellite imagery
Sedrata Watershed
Online adgang:https://doi.org/10.1038/s41598-026-43262-9
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