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

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Pubblicato in:	Water Resources Research vol. 61, no. 10 (Oct 1, 2025)
Autore principale:	Yin, Xiaoran
Altri autori:	Shu, Longcang, Wang, Zhe, Zhou, Long, Niu, Shuyao, Ren, Huazhun, Liu, Bo, Lu, Chengpeng
Pubblicazione:	John Wiley & Sons, Inc.
Soggetti:	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
Accesso online:	Citation/Abstract Full Text Full Text - PDF
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