{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:31:28Z","timestamp":1772451088865,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["41974032"],"award-info":[{"award-number":["41974032"]}]},{"name":"Science and Technology Department of Sichuan Province, China","award":["2020YJ0362"],"award-info":[{"award-number":["2020YJ0362"]}]},{"name":"Sichuan Society of Surveying, Mapping and Geoinformatics, China","award":["CCX202114"],"award-info":[{"award-number":["CCX202114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The volatility of the cumulative displacement of landslides is related to the influence of external factors. To improve the prediction of nonlinear changes in landslide displacement caused by external influences, a new combined forecasting model of landslide displacement has been proposed. Variational modal decomposition (VMD) was used to obtain the trend and fluctuation sequences of the original sequence of landslide displacement. First, we established a stacked long short time memory (LSTM) network model and introduced rainfall and reservoir water levels as influencing factors to predict the fluctuation sequence; next, we used a threshold autoregressive (TAR) model to predict the trend sequence, following which the trend and fluctuation prediction sequence were superimposed to obtain the cumulative predicted displacement of the landslide. Finally, the VMD-stacked LSTM-TAR combination model based on the variational modal decomposition, stacked long short time memory network, and a threshold autoregressive model was built. Taking the landslide of Baishuihe in the Three Gorges Reservoir area as an example, through comparison with the prediction results of the VMD-recurrent neural network-TAR, VMD-back propagation neural network-TAR, and VMD-LSTM-TAR, the proposed combined prediction model was noted to have high accuracy, and it provided a novel approach for the prediction of volatile landslide displacement.<\/jats:p>","DOI":"10.3390\/rs14051164","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Prediction of Landslide Displacement Based on the Combined VMD-Stacked LSTM-TAR Model"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9130-7355","authenticated-orcid":false,"given":"Yaping","family":"Gao","sequence":"first","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610054, China"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610054, China"}]},{"given":"Rui","family":"Tu","sequence":"additional","affiliation":[{"name":"National Times Service Center of Chinese Academy of Sciences, Xi\u2019an 710600, China"}]},{"given":"Guo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610054, China"}]},{"given":"Tong","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610054, China"}]},{"given":"Dongdong","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610054, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zarate, B.A., Hamdouni, R.E., and Fernandez, T. 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