Addressing Data Imbalance in Hydrological Machine Learning: Impact of Advanced Sampling Methods on Performance and Interpretability

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Detalles Bibliográficos
Publicado en:	Water Resources Research vol. 61, no. 10 (Oct 1, 2025)
Autor principal:	Yin, Xiaoran
Otros Autores:	Shu, Longcang, Wang, Zhe, Zhou, Long, Niu, Shuyao, Ren, Huazhun, Liu, Bo, Lu, Chengpeng
Publicado:	John Wiley & Sons, Inc.
Materias:	Hydrologic data Datasets Algorithms Sampling methods Soil properties Hydraulic conductivity Hydrology Machine learning Hypercubes Random sampling Performance evaluation Learning algorithms Contamination Accuracy Soil sciences Training Groundwater Statistical sampling Decision making National forests Hydraulics Latin hypercube sampling Environmental
Acceso en línea:	Citation/Abstract Full Text Full Text - PDF
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