Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models-Reference-Cited by-同舟云学术

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

Published:2019-04 Issue: Volume:114 Page:112-128
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Zaherpour Jamal^ORCID,Mount Nick,Gosling Simon N.^ORCID,Dankers Rutger^ORCID,Eisner Stephanie^ORCID,Gerten Dieter,Liu Xingcai^ORCID,Masaki Yoshimitsu,Müller Schmied Hannes^ORCID,Tang Qiuhong^ORCID,Wada Yoshihide

Funder

German Ministry of Education and Research

Islamic Development Bank, Saudi Arabia,

University of Nottingham Faculty of Social Sciences Research

Environment Research and Technology Development

Ministry of the Environment, Japan

Publisher

Elsevier BV

Subject

Ecological Modelling,Environmental Engineering,Software

Reference86 articles.

1. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments;Abrahart;Hydrol. Earth Syst. Sci.,2002

2. Multimodel combination techniques for analysis of hydrological simulations: application to Distributed Model Intercomparison Project results;Ajami;J. Hydrometeorol.,2006

3. An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction;Ajami;Water Resour. Res.,2007

4. A comprehensive surface-groundwater flow model;Arnold;J. Hydrol.,1993

5. A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation;Arsenault;J. Hydrol.,2015

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