High-resolution spatiotemporal analysis and driver attribution of floods in Kazakhstan using SHAP and remote sensing integration

The escalating impacts of global climate change and extreme weather have intensified flood risks worldwide, including in arid and semi-arid regions traditionally considered low-risk. This study examines the spatiotemporal dynamics of flood events across Kazakhstan from 2000 to 2024 by integrating re...

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Detalles Bibliográficos

Principais autores:	Yixuan Liu, Alim Samat, Peijun Du, Jin Chen, Jilili Abuduwaili, Kaiyue Luo, Enzhao Zhu, Dana Shokparova
Formato:	Artigo
Idioma:	Inglês
Publicado:	Elsevier 2026-03-01
Series:	Climate Risk Management
Assuntos:	Flood Spatiotemporal SHapley additive exPlanations (SHAP) Machine learning (ML) Kazakhstan Remote sensing
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S221209632500097X
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