An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility

An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected by city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations affected urban flooding. This study, taking entered on Shenzhen City, deploys an XGBoos...

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Bibliographic Details
Main Authors: Mo Wang, Yingxin Li, Haojun Yuan, Shiqi Zhou, Yuankai Wang, Rana Muhammad Adnan Ikram, Jianjun Li
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:Ecological Indicators
Subjects:
Urban flooding
Building configuration
XGBoost
SHapley Additive exPlanation
Explainable artificial intelligence
Adaptive spatial planning
Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X23012797

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http://www.sciencedirect.com/science/article/pii/S1470160X23012797

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