Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models
文献类型：期刊论文
| 作者 | Zaherpour, Jamal1; Mount, Nick1; Gosling, Simon N.1; Dankers, Rutger2; Eisner, Stephanie3,11; Gerten, Dieter4,5; Liu, Xingcai6 ; Masaki, Yoshimitsu7; Schmied, Hannes Mueller8,9; Tang, Qiuhong6
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| 刊名 | ENVIRONMENTAL MODELLING & SOFTWARE
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| 出版日期 | 2019-04-01 |
| 卷号 | 114页码:112-128 |
| 关键词 | Machine learning Model weighting Gene expression programming Global hydrological models Optimisation |
| ISSN号 | 1364-8152 |
| DOI | 10.1016/j.envsoft.2019.01.003 |
| 通讯作者 | Zaherpour, Jamal(zaherpour@gmail.com) |
| 英文摘要 | 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 ensemble mean (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. |
| WOS关键词 | CLIMATE-CHANGE IMPACTS ; WATER-QUALITY MODEL ; INTEGRATED MODEL ; DATA FUSION ; RUNOFF ; SCALE ; STREAMFLOW ; UNCERTAINTY ; SIMULATIONS ; PREDICTIONS |
| 资助项目 | German Ministry of Education and Research[01LS1201A] ; Islamic Development Bank, Saudi Arabia ; 2018 University of Nottingham Faculty of Social Sciences Research Outputs Award ; Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan |
| WOS研究方向 | Computer Science ; Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000458135500009 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | German Ministry of Education and Research ; Islamic Development Bank, Saudi Arabia ; 2018 University of Nottingham Faculty of Social Sciences Research Outputs Award ; Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/49863]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zaherpour, Jamal |
| 作者单位 | 1.Univ Nottingham, Sch Geog, Sir Clive Granger Bldg, Nottingham NG7 2RD, England 2.Met Off, FitzRoy Rd, Exeter EX1 3PB, Devon, England 3.Univ Kassel, Ctr Environm Syst Res, Wilhelmshoher Allee 47, D-34109 Kassel, Germany 4.Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany 5.Humboldt Univ, Dept Geog, D-10099 Berlin, Germany 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 7.Hirosaki Univ, Bunkyocho 3, Hirosaki, Aomori 0368561, Japan 8.Goethe Univ Frankfurt, Inst Phys Geog, Altenoferallee 1, D-60438 Frankfurt, Germany 9.Senckenberg Biodivers & Climate Res Ctr SBiK F, Senckenberganlage 25, D-60325 Frankfurt, Germany 10.IIASA, Schlosspl 1, A-2361 Laxenburg, Austria |
| 推荐引用方式 GB/T 7714 | Zaherpour, Jamal,Mount, Nick,Gosling, Simon N.,et al. Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models[J]. ENVIRONMENTAL MODELLING & SOFTWARE,2019,114:112-128. |
| APA | Zaherpour, Jamal.,Mount, Nick.,Gosling, Simon N..,Dankers, Rutger.,Eisner, Stephanie.,...&Wada, Yoshihide.(2019).Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models.ENVIRONMENTAL MODELLING & SOFTWARE,114,112-128. |
| MLA | Zaherpour, Jamal,et al."Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models".ENVIRONMENTAL MODELLING & SOFTWARE 114(2019):112-128. |
入库方式： OAI收割
来源：地理科学与资源研究所
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