Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area

Author:

Mo Chongxun1234,Yan Zhiwei1234ORCID,Ma Rongyong1234,Lei Xingbi1234ORCID,Deng Yun5,Lai Shufeng1234,Huang Keke1234,Mo Xixi12

Affiliation:

1. Key Laboratory of Disaster Prevention and Structural Safety, Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China

2. College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China

3. Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China

4. Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530004, China

5. Nanning Survey and Design Institute of Guangxi Pearl River Commission, Nanning 530004, China

Abstract

As the runoff series exhibit nonlinear and nonstationary characteristics, capturing the embedded periodicity and regularity in the runoff series using a single model is challenging. To account for these runoff characteristics and enhance the forecasting precision, this research proposed a new empirical wavelet transform–particle swarm optimization–support vector machine (EWT–PSO–SVM) hybrid model based on “decomposition-forecasting-reconstruction” for runoff forecasting and investigated its effectiveness in the karst area. First, empirical wavelet transform (EWT) was employed to decompose the original runoff series into multiple subseries. Second, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was applied to forecast every signal subseries. Finally, this study summarized the predictions of the subseries to reconstruct the ultimate runoff forecasting. The developed forecasting model was assessed by applying the monthly runoff series of the Chengbi River Karst Basin, and the composite rating index combined with five metrics was adopted as the performance evaluation tool. From the results of this research, it is clear that the EWT–PSO–SVM model outperforms both the PSO–SVM model and the SVM model in terms of the composite rating index, reaching 0.68. Furthermore, verifying the performance stability, the developed model was also compared with PSO–SVM and SVM models under different input data structures. The comparison demonstrated that the hybrid EWT–PSO–SVM model had a robust performance superiority and was an effective model that can be applied to karst area runoff forecasting.

Funder

National Natural Science Foundation of China

Guangxi University

Interdisciplinary Scientific Research Foundation of Guangxi University

Guangxi Water Resource Technology Promotion Foundation

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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