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Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case\u2014stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD\u2013ETS\u2013TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction.<\/jats:p>","DOI":"10.3390\/rs15010229","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T02:44:03Z","timestamp":1672627443000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["A Novel Hybrid LMD\u2013ETS\u2013TCN Approach for Predicting Landslide Displacement Based on GPS Time Series Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Wanqi","family":"Luo","sequence":"first","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5930-199X","authenticated-orcid":false,"given":"Jie","family":"Dou","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"},{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghu","family":"Fu","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Humanities and Law, Jiangsu Ocean University, Lianyungang 222005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0065-404X","authenticated-orcid":false,"given":"Xiekang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujian","family":"He","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Ma","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Xing","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137320","DOI":"10.1016\/j.scitotenv.2020.137320","article-title":"Different Sampling Strategies for Predicting Landslide Susceptibilities Are Deemed Less Consequential with Deep Learning","volume":"720","author":"Dou","year":"2020","journal-title":"Sci. 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