| Construction of runoff frequency division prediction model based on BP and LSSVM |
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| DOI：10.7606/j.issn.1000-7601.2024.03.27 |
| Key Words: runoff prediction empirical mode decomposition variational mode decomposition sample entropy neural network support vector machine |
| Author Name | Affiliation | | ZHANG Binglin | ollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China | | LI Jun | School of Ecology and Environment, Hainan University, Haikou, Hainan 570100, China | | SONG Songbai | ollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| This paper proposes a short\|term monthly runoff prediction hybrid model, CEEMDAN-VMD-(BP, LSSVM)-LSSVM, to address the strong randomness and volatility characteristics of runoff sequences. Firstly, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the runoff sequence into high\|frequency, mid\|frequency, and low\|frequency components. Then, the variational mode decomposition (VMD) method was further applied to decompose the high\|frequency component, and the obtained sub\|sequences from the two decompositions were integrated based on sample entropy. The back\|propagation neural network (BP) optimized by the sparrow search algorithm and the least square support vector machine (LSSVM) were employed to predict the high\|frequency and mid\|low\|frequency components, respectively. Finally, the fitting values of different frequency components during the training period were used as inputs for LSSVM to obtain the final runoff prediction. The proposed model was applied to the monthly runoff prediction at Yingluoxia and Qilian stations in the Heihe River Basin. The correlation coefficients and Nash efficiency coefficients during the verification period are both above 0.99. Compared with other eight models, this model demonstrates better prediction accuracy and can be applied to practical short\|term monthly runoff prediction. |
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