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Technology","award":["DHYC-202304"],"award-info":[{"award-number":["DHYC-202304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.<\/jats:p>","DOI":"10.3390\/rs16213978","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T07:04:04Z","timestamp":1730099044000},"page":"3978","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiwen","family":"Sun","sequence":"first","affiliation":[{"name":"National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Nanchang 330013, China"},{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}]},{"given":"Tieding","family":"Lu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Nanchang 330013, China"},{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}]},{"given":"Shunqiang","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China"}]},{"given":"Haicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hebei Institute of Investigation and Design of Water Conservancy and Hydropower Co., Ltd., Shijiazhuang 050085, China"}]},{"given":"Ziyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Xuzhou Surveying & Mapping Research Institute Co., Ltd., Xuzhou 221000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9956-4380","authenticated-orcid":false,"given":"Xiaoxing","family":"He","sequence":"additional","affiliation":[{"name":"School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, No. 86, Hongqi Ave., Ganzhou 341000, China"}]},{"given":"Hongqiang","family":"Ding","sequence":"additional","affiliation":[{"name":"Hebei Water Conservancy Engineering Bureau Group Limited, Shijiazhuang 050021, China"}]},{"given":"Yuntao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei Water Conservancy Engineering Bureau Group Limited, Shijiazhuang 050021, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"ref_1","first-page":"136","article-title":"Dam deformation prediction model based on singular spectrum analysis and improved whale optimization algorithm-optimized BP neural network","volume":"42","author":"Wang","year":"2023","journal-title":"J. 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