{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T13:19:03Z","timestamp":1784380743934,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2013CB733205"],"award-info":[{"award-number":["2013CB733205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012326","name":"International Science and Technology Cooperation Programme","doi-asserted-by":"publisher","award":["2018YFE0206500"],"award-info":[{"award-number":["2018YFE0206500"]}],"id":[{"id":"10.13039\/501100012326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction.<\/jats:p>","DOI":"10.3390\/rs13061055","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T20:51:42Z","timestamp":1615409502000},"page":"1055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1271-7968","authenticated-orcid":false,"given":"Guigen","family":"Nie","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center for Geospatial Information Technology, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Chinese Antarctic Center of Surveying and Mapping, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuguang","family":"Wu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Li","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobing","family":"Ren","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1130\/G33217.1","article-title":"Global patterns of loss of life from landslides","volume":"40","author":"Petley","year":"2012","journal-title":"Geology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bejar-Pizarro, M., Notti, D., Mateos, R.M., Ezquerro, P., Centolanza, G., Herrera, G., Bru, G., Sanabria, M., Solari, L., and Duro, J. (2017). Mapping Vulnerable Urban Areas Affected by Slow-Moving Landslides Using Sentinel-1 InSAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9090876"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide risk assessment and management: An overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5194\/nhess-13-85-2013","article-title":"Brief communication \u201cLandslide Early Warning System: Toolbox and general concepts\u201d","volume":"13","author":"Intrieri","year":"2013","journal-title":"Nat. Hazard. Earth Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10346-005-0049-1","article-title":"Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain)","volume":"2","author":"Corominas","year":"2005","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.enggeo.2017.01.016","article-title":"Landslide displacement prediction based on multivariate chaotic model and extreme learning machine","volume":"218","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10346-010-0215-y","article-title":"Monitoring, prediction, and early warning using ground-based radar interferometry","volume":"7","author":"Casagli","year":"2010","journal-title":"Landslides"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1007\/s12665-014-3764-x","article-title":"Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China","volume":"73","author":"Ren","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1061\/(ASCE)0733-9399(2009)135:4(276)","article-title":"Return Mapping Algorithms and Stress Predictors for Failure Analysis in Geomechanics","volume":"135","author":"Huang","year":"2009","journal-title":"J. Eng. Mech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.ijrmms.2010.07.001","article-title":"A new practical method for prediction of geomechanical failure-time","volume":"47","author":"Mufundirwa","year":"2010","journal-title":"Int. J. Rock Mech. Min."},{"key":"ref_11","unstructured":"Saito, M. (1965, January 8\u201315). Forecasting the Time of Occurrence of a Slope Failure. Proceedings of the 6th International Conference on Soil Mechanics and Foundation Engineering, Montreal, QC, Canada."},{"key":"ref_12","unstructured":"Saito, M. (1969, January 13\u201316). Forecasting time of slope failure by tertiary creep. Proceedings of the 7th International Conference on Soil Mechanics and Foundation Engineering, Mexico City, Mexico."},{"key":"ref_13","unstructured":"Hoek, E., and Bray, J. (1977). Rock Slope Engineering, Publication of Institution of Mining & Metallurgy. [Revised 2nd ed.]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.enggeo.2016.02.012","article-title":"Finite element simulation of an excavation-triggered landslide using large deformation theory","volume":"205","author":"Mohammadi","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s10346-013-0416-2","article-title":"Integration of a limit-equilibrium model into a landslide early warning system","volume":"11","author":"Thiebes","year":"2014","journal-title":"Landslides"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1007\/s10346-017-0804-0","article-title":"Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: A case study in the Three Gorges Reservoir area, China","volume":"14","author":"Ma","year":"2017","journal-title":"Landslides"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1007\/s10346-018-1022-0","article-title":"Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method","volume":"15","author":"Zhou","year":"2018","journal-title":"Landslides"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3825","DOI":"10.1007\/s00521-017-2968-x","article-title":"A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides","volume":"30","author":"Zhu","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201331). Speech Recognition with Deep Recurrent Neural Networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_20","unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/TNN.2009.2036174","article-title":"Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling","volume":"21","author":"Mirikitani","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012, February 01). Supervised Sequence Labelling with Recurrent Neural Networks. Available online: https:\/\/doi.org\/10.1007\/978-3-642-24797-2.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"54305","DOI":"10.1109\/ACCESS.2019.2912419","article-title":"The Application of Long Short-Term Memory (LSTM) Method on Displacement Prediction of Multifactor-Induced Landslides","volume":"7","author":"Xie","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1109\/ACCESS.2019.2961295","article-title":"Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network","volume":"8","author":"Xing","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xing, Y., Yue, J., Chen, C., Cong, K., Zhu, S., and Bian, Y. (2019). Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network. Appl. Sci., 9.","DOI":"10.3390\/app9152951"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s10346-018-01127-x","article-title":"Time series analysis and long short-term memory neural network to predict landslide displacement","volume":"16","author":"Yang","year":"2019","journal-title":"Landslides"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Parikh, A., T\u00e4ckstr\u00f6m, O., Das, D., and Uszkoreit, J. (2016). A Decomposable Attention Model for Natural Language Inference. arXiv.","DOI":"10.18653\/v1\/D16-1244"},{"key":"ref_32","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Luong, M., Pham, H., and Manning, C. (2015). Effective Approaches to Attention-based Neural Machine Translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"163926","DOI":"10.1109\/ACCESS.2020.3022177","article-title":"MSCNN-AM: A Multi-Scale Convolutional Neural Network with Attention Mechanisms for Retinal Vessel Segmentation","volume":"8","author":"Fu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2020.03.080","article-title":"A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction","volume":"403","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.neucom.2020.04.110","article-title":"Interpretable spatio-temporal attention LSTM model for flood forecasting","volume":"403","author":"Ding","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1061\/(ASCE)0733-9410(1995)121:1(43)","article-title":"Constitutive modeling and analysis of creeping slopes","volume":"121","author":"Desai","year":"1995","journal-title":"J. Geotech. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.enggeo.2016.02.009","article-title":"Application of time series analysis and PSO\u2013SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China","volume":"204","author":"Zhou","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56405-y","article-title":"Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model","volume":"9","author":"Li","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s10346-019-01314-4","article-title":"Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model","volume":"17","author":"Guo","year":"2020","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cageo.2017.10.013","article-title":"Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China","volume":"111","author":"Xu","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 16\u201320). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Honolulu, HI, USA.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.bspc.2014.06.009","article-title":"Improved complete ensemble EMD: A suitable tool for biomedical signal processing","volume":"14","author":"Colominas","year":"2014","journal-title":"Biomed. Signal Proces."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.enconman.2017.01.022","article-title":"A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting","volume":"136","author":"Zhang","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"O9","DOI":"10.1190\/geo2012-0199.1","article-title":"Empirical mode decomposition for seismic time-frequency analysis","volume":"78","author":"Han","year":"2013","journal-title":"Geophysics"},{"key":"ref_47","unstructured":"Raffel, C., and Ellis, D.P.W. (2015). Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:33:36Z","timestamp":1760160816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,10]]},"references-count":47,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061055"],"URL":"https:\/\/doi.org\/10.3390\/rs13061055","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,10]]}}}