A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework
文献类型：期刊论文
| 作者 | Meng, Erhao1; Huang, Shengzhi1; Huang, Qiang1; Fang, Wei1; Wang, Hao2; Leng, Guoyong3; Wang, Lu1; Liang, Hao1 |
| 刊名 | WATER RESOURCES MANAGEMENT
 |
| 出版日期 | 2021-03-01 |
| 页码 | 17 |
| 关键词 | Monthly streamflow prediction Support vector machine Variational mode decomposition Climate change Decomposition |
| ISSN号 | 0920-4741 |
| DOI | 10.1007/s11269-021-02786-7 |
| 通讯作者 | Huang, Shengzhi(huangshengzhi7788@126.com) |
| 英文摘要 | Some previous studies have proved that prediction models using traditional overall decomposition sampling (ODS) strategy are unreasonable because the subseries obtained by the ODS strategy contain future information to be predicted. It is, therefore, necessary to put forward a new sampling strategy to fix this defect and also to improve the accuracy and reliability of decomposition-based models. In this paper, a stepwise decomposition sampling (SDS) strategy according to the practical prediction process is introduced. Moreover, an innovative input selection framework is proposed to build a strong decomposition-based monthly streamflow prediction model, in which sunspots and atmospheric circulation anomaly factors are employed as candidate input variables to enhance the prediction accuracy of monthly streamflow in addition to regular inputs such as precipitation and evaporation. Meanwhile, the partial correlation algorithm is employed to select optimal input variables from candidate input variables including precipitation, evaporation, sunspots, and atmospheric circulation anomaly factors. Four basins of the U.S. MOPEX project with various climate characteristics were selected as a case study. Results indicate that: (1) adding teleconnection factors into candidate input variables helps enhance the prediction accuracy of the support vector machine (SVM) model in predicting streamflow; (2) the innovative input selection framework helps to improve the prediction capacity of models whose candidate input variables interact with each other compared with traditional selection strategy; (3) the SDS strategy can effectively prevent future information from being included into input variables, which is an appropriate substitute of the ODS strategy in developing prediction models; (4) as for monthly streamflow, the hybrid variable model decomposition-support vector machine (VMD-SVM) models, using an innovative input selection framework and the SDS strategy, perform better than those which have not adopted this framework in all study areas. Generally, the findings of this study showed that the hybrid VMD-SVM model combining the SDS strategy and innovative input selection framework is a useful and powerful tool for practical hydrological prediction work in the context of climate change. |
| 资助项目 | National Key Research and Development Program of China[2017YFC0405900] ; National Natural Science Foundation of China[51709221] ; Planning Project of Science and Technology of Water Resources of Shaanxi[2015slkj-27] ; Planning Project of Science and Technology of Water Resources of Shaanxi[2017slkj-19] ; China Scholarship Council[201908610170] ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research)[IWHR-SKL-KF201803] ; Doctorate Innovation Funding of Xi'an University of Technology[310-252072002] |
| WOS研究方向 | Engineering ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000623745000001 |
| 出版者 | SPRINGER |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Planning Project of Science and Technology of Water Resources of Shaanxi ; China Scholarship Council ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) ; Doctorate Innovation Funding of Xi'an University of Technology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/160557]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Shengzhi |
| 作者单位 | 1.Xian Univ Technol, State Key Lab Base Ecohydraul Engn Arid Area, Xian 710048, Peoples R China 2.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China 3.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Meng, Erhao,Huang, Shengzhi,Huang, Qiang,et al. A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework[J]. WATER RESOURCES MANAGEMENT,2021:17. |
| APA | Meng, Erhao.,Huang, Shengzhi.,Huang, Qiang.,Fang, Wei.,Wang, Hao.,...&Liang, Hao.(2021).A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework.WATER RESOURCES MANAGEMENT,17. |
| MLA | Meng, Erhao,et al."A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework".WATER RESOURCES MANAGEMENT (2021):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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