{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:55:47Z","timestamp":1783641347677,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2019YFC1509602"],"award-info":[{"award-number":["2019YFC1509602"]}]},{"name":"National Key R &amp; D Program of China","award":["42172303"],"award-info":[{"award-number":["42172303"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFC1509602"],"award-info":[{"award-number":["2019YFC1509602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42172303"],"award-info":[{"award-number":["42172303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The deformation of landslides is a non-linear dynamic and complex process due to the impacts of both inherent and external factors. Understanding the basis of landslide deformation is essential to prevent damage to properties and losses of life. To forecast the landslides displacement, a hybrid machine learning model is proposed, in which the Variational Modal Decomposition (VMD) is implemented to decompose the measured total surface displacement into the trend and periodic components. The Double Exponential Smoothing algorithm (DES) and Extreme Learning Machine (ELM) were adopted to predict the trend and the periodic displacement, respectively. Particle Swarm Optimization (PSO) algorithm was selected to obtain the optimal ELM model. The proposed method and implementation procedures were illustrated by a step-like landslide in the Three Gorges Reservoir area. For comparison, Least Square Support Vector Machine (LSSVM) and Convolutional Neutral Network\u2013Gated Recurrent Unit (CNN\u2013GRU) were also conducted with the same dataset to forecast the periodic component. The application results show that DES-PSO-ELM outperformed the other two methods in landslide displacement prediction, with RMSE, MAE, MAPE, and R2 values of 1.295mm, 0.998 mm, 0.008%, and 0.999, respectively.<\/jats:p>","DOI":"10.3390\/rs14143384","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T01:57:11Z","timestamp":1657850231000},"page":"3384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Hybrid Machine Learning Model Coupling Double Exponential Smoothing and ELM to Predict Multi-Factor Landslide Displacement"],"prefix":"10.3390","volume":"14","author":[{"given":"Xing","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4582-1369","authenticated-orcid":false,"given":"Fuling","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Hubei Province Disaster Prevention and Mitigation Engineering Technology Research Center, China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maolin","family":"Deng","sequence":"additional","affiliation":[{"name":"Hubei Province Disaster Prevention and Mitigation Engineering Technology Research Center, China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoqing","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wengang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, F., and Li, T. 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