Journal of System Simulation
Abstract
Abstract: Broad learning system(BLS) is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization. To reduce the influence of noise on the prediction results, the variational mode decomposition (VMD) is adopted to transform the onedimensional time-domain runoff signal to the two-dimensional time-frequency plane. The runoff prediction model based on VMD-LSTM-BLS is proposed. The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.
Recommended Citation
Han, Ying; Wang, Lehao; Wang, Shumei; Zhang, Xiang; and Luo, Xingxing
(2024)
"Runoff Intelligent Prediction Method Based on Broad-deep Fusion Time-frequency Analysis,"
Journal of System Simulation: Vol. 36:
Iss.
2, Article 7.
DOI: 10.16182/j.issn1004731x.joss.22-1137
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss2/7
First Page
363
Last Page
372
CLC
TP391.9
Recommended Citation
Han Ying, Wang Lehao, Wang Shumei, et al. Runoff Intelligent Prediction Method Based on Broaddeep Fusion Time-frequency Analysis[J]. Journal of System Simulation, 2024, 36(2): 363-372.
DOI
10.16182/j.issn1004731x.joss.22-1137
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons