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

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Detaylı Bibliyografya
Yayımlandı:	Water Resources Research vol. 61, no. 10 (Oct 1, 2025)
Yazar:	Yin, Xiaoran
Diğer Yazarlar:	Shu, Longcang, Wang, Zhe, Zhou, Long, Niu, Shuyao, Ren, Huazhun, Liu, Bo, Lu, Chengpeng
Baskı/Yayın Bilgisi:	John Wiley & Sons, Inc.
Konular:	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 Erişim:	Citation/Abstract Full Text Full Text - PDF
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