{"title":"基于 AIW-CLPSO 的 LSTM 神经网络闸前水位预测模型","authors":"Linqing Gao, Dengzhe Ha, Litao Ma, Jiqiang Chen","doi":"10.1007/s10878-023-01101-x","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The prediction model of water level in front of the check gate of the LSTM neural network based on AIW-CLPSO\",\"authors\":\"Linqing Gao, Dengzhe Ha, Litao Ma, Jiqiang Chen\",\"doi\":\"10.1007/s10878-023-01101-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":50231,\"journal\":{\"name\":\"Journal of Combinatorial Optimization\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Combinatorial Optimization\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s10878-023-01101-x\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-023-01101-x","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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. 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.
期刊介绍:
The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering.
The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.