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A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework

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Abstract

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.

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Acknowledgements

This study was jointly funded by the National Key Research and Development Program of China (grant number 2017YFC0405900), the National Natural Science Foundation of China (grant number 51709221), the Planning Project of Science and Technology of Water Resources of Shaanxi (grant numbers 2015slkj-27 and 2017slkj-19), the China Scholarship Council (grant number 201908610170), the 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, grant number IWHR-SKL-KF201803) and the Doctorate Innovation Funding of Xi’an University of Technology (grant number 310-252072002).

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Authors and Affiliations

  1. State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, 710048, Xi’an, China

    Erhao Meng, Shengzhi Huang, Qiang Huang, Wei Fang, Lu Wang & Hao Liang

  2. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

    Hao Wang

  3. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Guoyong Leng

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  1. Erhao Meng
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  2. Shengzhi Huang
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  3. Qiang Huang
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  4. Wei Fang
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  5. Hao Wang
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  6. Guoyong Leng
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  7. Lu Wang
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  8. Hao Liang
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Contributions

Conceptualization: S. H; E. M; H. W. Methodology: E. M; G. L; W. F; L. W. Formal analysis and investigation: E. M; H. L. Writing - original draft preparation: E. M. Writing - review and editing: E. M; L. C. Supervision: Q. H.

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Correspondence to Shengzhi Huang.

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Meng, E., Huang, S., Huang, Q. et al. A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework. Water Resour Manage 35, 1321–1337 (2021). https://doi.org/10.1007/s11269-021-02786-7

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