{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T17:39:40Z","timestamp":1781804380195,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Provincial Department of Water Resources","award":["2022050"],"award-info":[{"award-number":["2022050"]}]},{"name":"Jiangsu Provincial Department of Water Resources","award":["2022083"],"award-info":[{"award-number":["2022083"]}]},{"name":"Jiangsu Provincial Department of Water Resources","award":["2023z036"],"award-info":[{"award-number":["2023z036"]}]},{"name":"Jiangsu Provincial Department of Water Resources","award":["2023z045"],"award-info":[{"award-number":["2023z045"]}]},{"name":"Jiangsu Hydraulic Research Institute","award":["2022050"],"award-info":[{"award-number":["2022050"]}]},{"name":"Jiangsu Hydraulic Research Institute","award":["2022083"],"award-info":[{"award-number":["2022083"]}]},{"name":"Jiangsu Hydraulic Research Institute","award":["2023z036"],"award-info":[{"award-number":["2023z036"]}]},{"name":"Jiangsu Hydraulic Research Institute","award":["2023z045"],"award-info":[{"award-number":["2023z045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies.<\/jats:p>","DOI":"10.3390\/rs16101767","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T09:30:03Z","timestamp":1715851803000},"page":"1767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Yilin","family":"Xie","sequence":"first","affiliation":[{"name":"Jiangsu Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-8054","authenticated-orcid":false,"given":"Xiaolin","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Instrument Science and engineering, Southeast University, Nanjing 210096, China"},{"name":"Faculty of Engineering, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4141-4234","authenticated-orcid":false,"given":"Haiyang","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Lu","sequence":"additional","affiliation":[{"name":"Jiangsu Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfeng","family":"Ding","sequence":"additional","affiliation":[{"name":"Jiangsu Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushan","family":"Jia","sequence":"additional","affiliation":[{"name":"The Management Office of Shilianghe Reservoir in Lianyungang City, Lianyungang 222300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"ref_1","first-page":"317","article-title":"Hydrological safety and risk assessment of hydraulic structures","volume":"19","author":"Strygina","year":"2018","journal-title":"RUDN J. 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