Tail-Aware Forecasting of Precipitation Extremes Using STL-GEV and LSTM Neural Networks

Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not on...

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Detalles Bibliográficos

Principais autores:	Haoyu Niu, Samantha Murray, Fouad Jaber, Bardia Heidari, Nick Duffield
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2025-10-01
Series:	Hydrology
Assuntos:	extreme precipitation deep learning LSTM GEV distribution tail-weighted loss hydrological forecasting
Acceso en liña:	https://www.mdpi.com/2306-5338/12/11/284
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