Exploring the Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models

Abstract Studying streamflow processes and controlling factors is crucial for sustainable water resource management. This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Ra...

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Detalles Bibliográficos

Principais autores:	Bingbing Ding, Xinxiao Yu, Guodong Jia
Formato:	Artigo
Idioma:	Inglês
Publicado:	Wiley 2025-05-01
Series:	Water Resources Research
Assuntos:	streamflow variability climate change land use change machine learning hydrological modeling
Acceso en liña:	https://doi.org/10.1029/2024WR039734
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