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

Juchao Zhaoa School of Geography, Guangdong Engineering Technology Research Center of Smart land, South China Normal University, Guangzhou, China;b Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaView further author information

Chunbo Zhanga School of Geography, Guangdong Engineering Technology Research Center of Smart land, South China Normal University, Guangzhou, China;b Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaView further author information

Jin Wangb Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, China;c Beidou Research Institute, South China Normal University, Foshan, ChinaView further author information

Zaheer Abbasa School of Geography, Guangdong Engineering Technology Research Center of Smart land, South China Normal University, Guangzhou, China;b Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaView further author information

Yaolong Zhaoa School of Geography, Guangdong Engineering Technology Research Center of Smart land, South China Normal University, Guangzhou, China;b Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, ChinaCorrespondence[email protected]
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Machine learning and SHAP-based susceptibility assessment of storm flood in rapidly urbanizing areas: a case study of Shenzhen, China

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

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