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Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models 期刊论文
ENVIRONMENTAL MODELLING & SOFTWARE, 2019, 卷号: 114, 页码: 112-128
Authors:  Zaherpour, Jamal;  Mount, Nick;  Gosling, Simon N.;  Dankers, Rutger;  Eisner, Stephanie;  Gerten, Dieter;  Liu, Xingcai;  Masaki, Yoshimitsu;  Schmied, Hannes Mueller;  Tang, Qiuhong;  Wada, Yoshihide
Favorite  |  View/Download:10/0  |  Submit date:2019/05/22
Machine learning  Model weighting  Gene expression programming  Global hydrological models  Optimisation  
Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2018, 卷号: 13, 期号: 5, 页码: 16
Authors:  Veldkamp, T. I. E.;  Zhao, F.;  Ward, P. J.;  de Moel, H.;  Aerts, J. C. J. H.;  Schmied, H. Mueller;  Portmann, F. T.;  Masaki, Y.;  Pokhrel, Y.;  Liu, X.;  Satoh, Y.;  Gerten, D.;  Gosling, S. N.;  Zaherpour, J.;  Wada, Y.
Favorite  |  View/Download:9/0  |  Submit date:2019/05/23
hydrological extremes  human impact  validation  global hydrological modeling  multi-model  fresh water resources