| 来源类型 | Article |
| 规范类型 | 其他 |
| DOI | 10.1016/j.envsoft.2019.01.003 |
| Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. | |
| Zaherpour J; Mount N; Gosling S; Dankers R; Eisner S; Gerten D; Liu X; Masaki Y | |
| 发表日期 | 2019 |
| 出处 | Environmental Modelling & Software 114: 112-128 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 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 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. |
| 主题 | Water (WAT) |
| 关键词 | Machine learning Model weighting Gene expression programming Global hydrological models Optimisation |
| URL | http://pure.iiasa.ac.at/id/eprint/15698/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/131688 |
| 推荐引用方式 GB/T 7714 | Zaherpour J,Mount N,Gosling S,et al. Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models.. 2019. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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