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Feature importance has been a popular approach for machine learning models to investigate the relative significance of model predictors. In this study, we developed a Wilk's feature importance (WFI) method for hydrological inference. Compared with co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ab4d4793b27038186c800bfa9161736
https://doi.org/10.5194/hess-2021-65
https://doi.org/10.5194/hess-2021-65
Autor:
Jia-mei Yao, X. L. Zhang, Weihong Tan, G. M. Zeng, Guohe Huang, R. J. Xiang, Guanjun Zhang, Yi-Min Jiang, Jianbing Li
Publikováno v:
Hydrology and Earth System Sciences. 10:65-77
The original Gash analytical model and the sparse Gash's model were combined to simulate rainfall interception losses from the top- and sub-canopy layers in Shaoshan evergreen forest located in central-south China in 2003. The total estimated interce