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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Vydané v:	Meteorological applications Ročník 32; číslo 3
Hlavní autori:	Cai, Yuexing, Huang, Cuiyin, Zheng, Fengqin, Li, Guangtao, Lai, Sheng, Zhu, Liyun, Zhu, Qiuyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Ltd 01.05.2025 Wiley
Predmet:	algorithm efficiency feature reduction physical consistency SHAP interpretability time‐lagged precursors tropical cyclone‐induced extreme rainfall
ISSN:	1350-4827, 1469-8080
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