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

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Bibliografski detalji
Izdano u:	Water Resources Research vol. 61, no. 10 (Oct 1, 2025)
Glavni autor:	Yin, Xiaoran
Daljnji autori:	Shu, Longcang, Wang, Zhe, Zhou, Long, Niu, Shuyao, Ren, Huazhun, Liu, Bo, Lu, Chengpeng
Izdano:	John Wiley & Sons, Inc.
Teme:	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
Online pristup:	Citation/Abstract Full Text Full Text - PDF
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