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

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Bibliografiset tiedot
Julkaisussa:	Water Resources Research vol. 61, no. 10 (Oct 1, 2025)
Päätekijä:	Yin, Xiaoran
Muut tekijät:	Shu, Longcang, Wang, Zhe, Zhou, Long, Niu, Shuyao, Ren, Huazhun, Liu, Bo, Lu, Chengpeng
Julkaistu:	John Wiley & Sons, Inc.
Aiheet:	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
Linkit:	Citation/Abstract Full Text Full Text - PDF
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