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A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition

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Abstract

Predicting runoff, one of the fundamental operations in hydrology, is crucial for directing the complete exploitation and use of local water resources. However, influenced by factors such as human activities and climate change, runoff displays typical nonlinear, non-stationary dynamic characteristics, which means it is challenging to achieve accurate runoff prediction in the research on water resources. In this research, we developed a hybrid model named CEEMDAN-FE-VMD-SVM-GRU for runoff prediction, which was built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FE), variational mode decomposition (VMD), support vector machine (SVM), and gate recurrent unit (GRU). First, CEEMDAN was used to decompose the original daily runoff dataset into several intrinsic mode functions (IMF), followed by the introduction of FE to compute the complexity of each IMF component. The obtained FE calculation results greater than 0.4 were set as high-frequency sequences, and those lower than 0.4 as low-frequency sequences. Then, VMD was applied to perform the secondary decomposition of the high-frequency sequences, and SVM and GRU were trained to predict the primary and secondary decomposition parts, respectively. The results were finally obtained through linear combination. In this study, the daily runoff of the Minjiang River by this model was compared with those of other eight models. The findings demonstrate that the proposed model worked better than other models in a variety of evaluation metrics. In addition, this model showed better applicability in uncertainty interval estimation and flood prediction. Hence, this model proposed in this study has potential to be a preferred data-driven tool in hydrological prediction.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

It was supported by the Open Fund of Key Laboratory of Sediment Science and Northern River Training, the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research (No. IWHR-SEDI-2023-10).

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Authors and Affiliations

  1. College of Marine Ecology and Enviroment, Shanghai Ocean University, Shanghai, 201306, People’s Republic of China

    Jinghan Dong

  2. College of Information, Shanghai Ocean University, Shanghai, 201306, People’s Republic of China

    Zhaocai Wang & Renlin Pei

  3. College of Economics and Management, Shanghai Ocean University, Shanghai, 201306, People’s Republic of China

    Junhao Wu

  4. College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, 201306, People’s Republic of China

    Xuefei Cui

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  1. Jinghan Dong
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  2. Zhaocai Wang
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  5. Renlin Pei
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Contributions

Jinghan Dong: Conceptualization, Writing—original draft, Methodology, Investigation, Visualization, Validation. Zhaocai Wang: Methodology, Investigation, Validation, Supervision, Writing-review. Junhao Wu: Writing—original draft, Investigation. Xuefei Cui: Investigation, Visualization. Renlin Pei: Writing—original draft.

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Correspondence to Zhaocai Wang.

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Dong, J., Wang, Z., Wu, J. et al. A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition. Water Resour Manage 38, 1655–1674 (2024). https://doi.org/10.1007/s11269-024-03748-5

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