SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting

SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting

Given the increased incidence of pluvial floods due to climate change and urbanization, the demand for highly efficient and accurate modeling within urban drainage systems has intensified, making machine learning and deep learning techniques increasingly popular. Nonetheless, these data-driven appro...

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Bibliographic Details
Main Authors: Wenhao Chu, Chunxiao Zhang, Heng Li, Laifu Zhang, Dingtao Shen, Rongrong Li
Format: Article
Language:English
Published: Elsevier 2024-07-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Urban flooding
Spatiotemporal effects
Bayesian neural network
Shapley additive explanation
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843224003261

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http://www.sciencedirect.com/science/article/pii/S1569843224003261

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