The prediction model of water level in front of the check gate of the LSTM neural network based on AIW-CLPSO

To solve the problem of predicting water level in front of check gate under different time scales, a different time scale prediction model with a long short term memory (LSTM) neural network based on adaptive inertia weight comprehensive learning particle swarm optimization (AIW-CLPSO) is proposed....

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Bibliographic Details
Published in:Journal of combinatorial optimization 2024-03, Vol.47 (2), Article 5
Main Authors: Gao, Linqing, Ha, Dengzhe, Ma, Litao, Chen, Jiqiang
Format: Article
Language:English
Subjects:
28th International Computing and Combinatorics Conference: COCOON 2022
Algorithms
Combinatorics
Convex and Discrete Geometry
Global optimization
Mathematical Modeling and Industrial Mathematics
Mathematical models
Mathematics
Mathematics and Statistics
Neural networks
Operations Research/Decision Theory
Optimization
Parameters
Particle swarm optimization
Prediction models
Predictions
Resource scheduling
Theory of Computation
Time
Water level fluctuations
Water resources
Water transfer
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Summary:To solve the problem of predicting water level in front of check gate under different time scales, a different time scale prediction model with a long short term memory (LSTM) neural network based on adaptive inertia weight comprehensive learning particle swarm optimization (AIW-CLPSO) is proposed. The AIW and CLPSO are adopted to improve the global optimization ability and convergence velocity of particle swarm optimization in the proposed model. The model was applied to the water level prediction in front of the Chaohu Lake check gate. The example of the water level prediction in front of the Chaohu Lake check gate shows that the proposed model predicts the trend of water level fluctuation better than LSTM with high accuracy of Nash coefficient up to 0.9851 and root mean square error up to 0.0273 m. The optimized algorithm can obtain the optimal parameters of the LSTM neural network, overcome the limitations of the traditional LSTM neural network in parameter selection and inaccurate prediction, and maintain good prediction results in the predicting water level in front of the check gate at different time scales.This study can provide important reference for water level prediction, scheduling warning, water resources scheduling decision and intelligent gate control in long distance water transfer projects.
ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-023-01101-x