A hybrid variational mode decomposition and sparrow search algorithm-based least square support vector machine model for monthly runoff forecasting

Monthly runoff forecasting has always been a key problem in water resources management. As a data-driven method, the least square support vector machine (LSSVM) method has been investigated by numerous studies in runoff forecasting. However, selecting appropriate parameters for LSSVM is the key to o...

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Bibliographic Details
Published in:Water science & technology. Water supply 2022-06, Vol.22 (6), p.5698-5715
Main Authors: Li, Bao-Jian, Sun, Guo-Liang, Li, Yu-Peng, Zhang, Xiao-Li, Huang, Xu-Dong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Correlation coefficient
Correlation coefficients
Decomposition
Forecasting
Hydrology
least square support vector machine (lssvm)
Least squares
Machine learning
Mathematical models
Monthly
monthly runoff forecasting
Optimization
Parameters
Performance evaluation
Performance indices
Runoff
Runoff forecasting
Search algorithms
sparrow search algorithm (ssa)
Statistical analysis
Statistics
Support vector machines
Time series
variational mode decomposition (vmd)
Water resources
Water resources management
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Summary:Monthly runoff forecasting has always been a key problem in water resources management. As a data-driven method, the least square support vector machine (LSSVM) method has been investigated by numerous studies in runoff forecasting. However, selecting appropriate parameters for LSSVM is the key to obtaining satisfactory model performance. In this study, we propose a hybrid model for monthly runoff forecasting, VMD-SSA-LSSVM for short, which combines variational mode decomposition (VMD) with LSSVM and the parameters of LSSVM are optimized by a sparrow search algorithm (SSA). Firstly, VMD is utilized to decompose the original time series data into several subsequences. Secondly, LSSVM is employed to simulate each subsequence, for which the parameters are optimized by SSA. Finally, the simulated results for each subsequence are accumulated as the final results. The validity of the proposed model was verified by forecasting monthly runoff for two reservoirs located in China. Four frequently-used statistical indexes, namely the Nash efficiency coefficient, root mean squared error, correlation coefficient and mean absolute percentage error were used to evaluate model performance. The results demonstrate the superiority of VMD-SSA-LSSVM over the compared models in terms of all statistical indexes, indicating that it is beneficial for enhancing monthly runoff forecast accuracy.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2022.136