Predicting Tropical Cyclone Extreme Rainfall in Guangxi, China: An Interpretable Machine Learning Framework Addressing Class Imbalance and Feature Optimization

ABSTRACT Accurate prediction of tropical cyclone‐induced extreme rainfall (TCER) is of utmost importance for disaster mitigation in coastal regions. However, it remains a formidable challenge due to the intricate interactions among multi‐scale meteorological factors and the inherent data imbalances....

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Veröffentlicht in:	Meteorological applications Jg. 32; H. 3
Hauptverfasser:	Cai, Yuexing, Huang, Cuiyin, Zheng, Fengqin, Li, Guangtao, Lai, Sheng, Zhu, Liyun, Zhu, Qiuyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester, UK John Wiley & Sons, Ltd 01.05.2025 Wiley
Schlagworte:	algorithm efficiency feature reduction physical consistency SHAP interpretability time‐lagged precursors tropical cyclone‐induced extreme rainfall
ISSN:	1350-4827, 1469-8080
Online-Zugang:	Volltext
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