Machine learning and SHAP-based susceptibility assessment of storm flood in rapidly urbanizing areas: a case study of Shenzhen, China

Machine learning and SHAP-based susceptibility assessment of storm flood in rapidly urbanizing areas: a case study of Shenzhen, China

In recent years, urban flooding disasters have occurred frequently. Conducting research on flood susceptibility assessment is critical for urban flood prevention and urban renewal planning. However, determining how to effectively improve the accuracy of flood susceptibility assessment remains a chal...

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Bibliographic Details
Main Authors:	Juchao Zhao, Chunbo Zhang, Jin Wang, Zaheer Abbas, Yaolong Zhao
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2024-12-01
Series:	Geomatics, Natural Hazards & Risk
Subjects:	Urban flood landscape pattern machine learning SHapely additive exPlanations feature preference
Online Access:	https://www.tandfonline.com/doi/10.1080/19475705.2024.2311889
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