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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86293 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86293 http://eprints.org/relation/ismediumThumbnailVersionOf https://pure.iiasa.ac.at/id/document/86293 86299 2 244800 document 86299 small.jpg image/png bd850fbe95db3d5bc49af53ad94b1b20 MD5 1719 2019-01-21 07:31:40 https://pure.iiasa.ac.at/id/eprint/15698/6/small.jpg 15698 6 6 image/png other Thumbnails conversion from text to thumbnail_small en staffonly

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86293 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86293 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/86293 archive 353 disk0/00/01/56/98 2019-01-21 07:26:41 2021-08-27 17:31:19 2019-01-21 07:26:41 article show Zaherpour J. Mount N. Gosling S. Dankers R. Eisner S. Gerten D. Liu X. Masaki Y. Müller H. Tang Q. Wada Y. 8940 0000-0003-4770-2539 pub prog_wat Machine learning; Model weighting; Gene expression programming; Global hydrological models; Optimisation 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 multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users 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. 2019-04-16 published Elsevier 10.1016/j.envsoft.2019.01.003 2601 public Environmental Modelling & Software 114 112-128 TRUE 1873-6726 FALSE no info:eu-repo/semantics/article