Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models
This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance g...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019
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| Online Access: | https://eprints.nottingham.ac.uk/55925/ |
| _version_ | 1848799239785676800 |
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| author | Zaherpour, Jamal Mount, Nick J. Gosling, Simon N. Dankers, Rutger Eisner, Stephanie Dieter, Gerten Liu, Xingcai Masaki, Yoshimitsu Müller Schmied, Hannes Tang, Qiuhong Wada, Yoshihide |
| author_facet | Zaherpour, Jamal Mount, Nick J. Gosling, Simon N. Dankers, Rutger Eisner, Stephanie Dieter, Gerten Liu, Xingcai Masaki, Yoshimitsu Müller Schmied, Hannes Tang, Qiuhong Wada, Yoshihide |
| author_sort | Zaherpour, Jamal |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained. |
| first_indexed | 2025-11-14T20:32:31Z |
| format | Article |
| id | nottingham-55925 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:32:31Z |
| publishDate | 2019 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-559252020-01-16T04:30:12Z https://eprints.nottingham.ac.uk/55925/ Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models Zaherpour, Jamal Mount, Nick J. Gosling, Simon N. Dankers, Rutger Eisner, Stephanie Dieter, Gerten Liu, Xingcai Masaki, Yoshimitsu Müller Schmied, Hannes Tang, Qiuhong Wada, Yoshihide This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained. Elsevier 2019-01-12 Article PeerReviewed application/pdf en cc_by_nc_nd https://eprints.nottingham.ac.uk/55925/1/Exploring%20the%20value%20of%20machine%20learning%20for%20weighted%20multi-model%20combination%20of%20an%20ensemble%20of%20...%20Plus%20SI_Zaherpour%20et%20al._ENVSOFT_2017.pdf Zaherpour, Jamal, Mount, Nick J., Gosling, Simon N., Dankers, Rutger, Eisner, Stephanie, Dieter, Gerten, Liu, Xingcai, Masaki, Yoshimitsu, Müller Schmied, Hannes, Tang, Qiuhong and Wada, Yoshihide (2019) Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. Environmental Modelling and Software . ISSN 1873-6726 (In Press) Machine Learning; Model Weighting; Gene Expression Programming; Global Hydrological Models; Optimization https://www.sciencedirect.com/science/article/pii/S1364815217309817 doi:10.1016/j.envsoft.2019.01.003 doi:10.1016/j.envsoft.2019.01.003 |
| spellingShingle | Machine Learning; Model Weighting; Gene Expression Programming; Global Hydrological Models; Optimization Zaherpour, Jamal Mount, Nick J. Gosling, Simon N. Dankers, Rutger Eisner, Stephanie Dieter, Gerten Liu, Xingcai Masaki, Yoshimitsu Müller Schmied, Hannes Tang, Qiuhong Wada, Yoshihide Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| title | Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| title_full | Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| title_fullStr | Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| title_full_unstemmed | Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| title_short | Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| title_sort | exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models |
| topic | Machine Learning; Model Weighting; Gene Expression Programming; Global Hydrological Models; Optimization |
| url | https://eprints.nottingham.ac.uk/55925/ https://eprints.nottingham.ac.uk/55925/ https://eprints.nottingham.ac.uk/55925/ |