{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:19:28Z","timestamp":1772605168344,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42090054"],"award-info":[{"award-number":["42090054"]}]},{"name":"National Natural Science Foundation of China","award":["52239006"],"award-info":[{"award-number":["52239006"]}]},{"name":"National Natural Science Foundation of China","award":["KY2021-ZD-03"],"award-info":[{"award-number":["KY2021-ZD-03"]}]},{"name":"National Natural Science Foundation of China","award":["2022CFA002"],"award-info":[{"award-number":["2022CFA002"]}]},{"name":"Key Science and Technology Plan Project of Power China Huadong Engineering Corporation Limited","award":["42090054"],"award-info":[{"award-number":["42090054"]}]},{"name":"Key Science and Technology Plan Project of Power China Huadong Engineering Corporation Limited","award":["52239006"],"award-info":[{"award-number":["52239006"]}]},{"name":"Key Science and Technology Plan Project of Power China Huadong Engineering Corporation Limited","award":["KY2021-ZD-03"],"award-info":[{"award-number":["KY2021-ZD-03"]}]},{"name":"Key Science and Technology Plan Project of Power China Huadong Engineering Corporation Limited","award":["2022CFA002"],"award-info":[{"award-number":["2022CFA002"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["42090054"],"award-info":[{"award-number":["42090054"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["52239006"],"award-info":[{"award-number":["52239006"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["KY2021-ZD-03"],"award-info":[{"award-number":["KY2021-ZD-03"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["2022CFA002"],"award-info":[{"award-number":["2022CFA002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Disasters caused by landslides pose a considerable threat to people\u2019s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results.<\/jats:p>","DOI":"10.3390\/rs15092296","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T04:30:47Z","timestamp":1682569847000},"page":"2296","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Weida","family":"Ni","sequence":"first","affiliation":[{"name":"Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China"},{"name":"Zhejiang Huadong Construction Engineering Corporation Limited, Hangzhou 310004, China"}]},{"given":"Liuyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China"},{"name":"Zhejiang Huadong Construction Engineering Corporation Limited, Hangzhou 310004, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0009-0000-8519-1421","authenticated-orcid":false,"given":"Lele","family":"Zhang","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ke","family":"Xing","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/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"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105267","DOI":"10.1016\/j.enggeo.2019.105267","article-title":"Geohazards in the Three Gorges Reservoir Area, China\u2014Lessons Learned from Decades of Research","volume":"261","author":"Tang","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s10064-014-0671-z","article-title":"Deformation Response of the Huangtupo Landslide to Rainfall and the Changing Levels of the Three Gorges Reservoir","volume":"74","author":"Tang","year":"2015","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s12583-012-0259-0","article-title":"Stability of Huangtupo Riverside Slumping Mass II# under Water Level Fluctuation of Three Gorges Reservoir","volume":"23","author":"Hu","year":"2012","journal-title":"J. Earth Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106051","DOI":"10.1016\/j.enggeo.2021.106051","article-title":"Geohazards and Human Settlements: Lessons Learned from Multiple Relocation Events in Badong, China\u2014Engineering Geologist\u2019s Perspective","volume":"285","author":"Gong","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105279","DOI":"10.1016\/j.enggeo.2019.105279","article-title":"Susceptibility of Reservoir-Induced Landslides and Strategies for Increasing the Slope Stability in the Three Gorges Reservoir Area: Zigui Basin as an Example","volume":"261","author":"Li","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_6","first-page":"103157","article-title":"International Journal of Applied Earth Observations and Geoinformation Dynamic Landslides Susceptibility Evaluation in Baihetan Dam Area during Extensive Impoundment by Integrating Geological Model and InSAR Observations","volume":"116","author":"Dai","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1766038","DOI":"10.1155\/2022\/1766038","article-title":"Understanding the Slow Motion of the Wangjiashan Landslide in the Baihetan Reservoir Area (China) from Space-Borne Radar Observations","volume":"2022","author":"Wu","year":"2022","journal-title":"Adv. Civ. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s10346-022-01898-4","article-title":"The Initial Impoundment of the Baihetan Reservoir Region (China) Exacerbated the Deformation of the Wangjiashan Landslide: Characteristics and Mechanism","volume":"19","author":"Yi","year":"2022","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106946","DOI":"10.1016\/j.catena.2023.106946","article-title":"Vegetation-Landslide Nexus and Topographic Changes Post the 2004 Mw 6.6 Chuetsu Earthquake","volume":"223","author":"Xiang","year":"2023","journal-title":"Catena"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Xu, Y., Ghamisi, P., Kopp, M., and Kreil, D. (2022). Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2022.3215209"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Xu, Y., Zhao, H., Wang, J., Zhong, Y., Zhao, D., Zang, Q., Wang, S., Zhang, F., and Shi, Y. (2022). The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15.","DOI":"10.1109\/JSTARS.2022.3220845"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105608","DOI":"10.1016\/j.enggeo.2020.105608","article-title":"Landslide Displacement Prediction Based on Multi-Source Data Fusion and Sensitivity States","volume":"271","author":"Liu","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102973","DOI":"10.1016\/j.earscirev.2019.102973","article-title":"Geographical Landslide Early Warning Systems","volume":"200","author":"Guzzetti","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.geomorph.2004.09.011","article-title":"The Use of Surface Monitoring Data for the Interpretation of Landslide Movement Patterns","volume":"66","author":"Petley","year":"2005","journal-title":"Geomorphology"},{"key":"ref_15","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. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Luo, W., Dou, J., Fu, Y., Wang, X., He, Y., Ma, H., Wang, R., and Xing, K. (2023). A Novel Hybrid LMD\u2013ETS\u2013TCN Approach for Predicting Landslide Displacement Based on GPS Time Series Analysis. Remote Sens., 15.","DOI":"10.3390\/rs15010229"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tang, H., Huang, J., Wen, T., Ma, J., and Zhang, J. (2022). A Comparative Study of Different Machine Learning Methods for Reservoir Landslide Displacement Prediction. Eng. Geol., 298.","DOI":"10.1016\/j.enggeo.2022.106544"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10346-012-0326-8","article-title":"Displacement Prediction in Colluvial Landslides, Three Gorges Reservoir, China","volume":"10","author":"Du","year":"2013","journal-title":"Landslides"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10346-009-0177-0","article-title":"Early Warning of Rainfall-Induced Shallow Landslides and Debris Flows in the USA","volume":"7","author":"Baum","year":"2010","journal-title":"Landslides"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Luo, H., Xu, Q., Lu, Z., Liao, L., Li, H., and Hao, L. (2022). A Graph Convolutional Incorporating GRU Network for Landslide Displacement Forecasting Based on Spatiotemporal Analysis of GNSS Observations. Remote Sens., 14.","DOI":"10.3390\/rs14041016"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhao, Y., Bai, L., Guo, W., and Meng, Q. (2021). Landslide Displacement Prediction Method Based on GA-Elman Model. Appl. Sci., 11.","DOI":"10.3390\/app112211030"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1007\/s10064-018-1237-2","article-title":"Identification of Movement Characteristics and Causal Factors of the Shuping Landslide Based on Monitored Displacements","volume":"78","author":"Wu","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.geomorph.2019.06.024","article-title":"Spatiotemporal Deformation Characteristics and Triggering Factors of Baijiabao Landslide in Three Gorges Reservoir Region, China","volume":"343","author":"Yao","year":"2019","journal-title":"Geomorphology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s10346-019-01312-6","article-title":"Modeling and Predicting Reservoir Landslide Displacement with Deep Belief Network and EWMA Control Charts: A Case Study in Three Gorges Reservoir","volume":"17","author":"Li","year":"2020","journal-title":"Landslides"},{"key":"ref_26","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_27","unstructured":"Saito, M. (1969, January 25\u201329). 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_28","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1126\/science.243.4888.200","article-title":"A Relation to Describe Rate-Dependent Material Failure","volume":"243","author":"Voight","year":"1989","journal-title":"Science"},{"key":"ref_29","unstructured":"L\u00e9vy, C., Gendrey, S., Bernardie, S., Chanut, M.-A., Vallet, A., Dubois, L., and Duranthon, J.-P. (2017). Advancing Culture of Living with Landslides: Volume 3 Advances in Landslide Technology, Springer."},{"key":"ref_30","unstructured":"Li, T.B., Chen, M.D., and Wang, L.S. (1999). Landslide Real-Time Tracking Prediction, Chengdu University of Science and Technology Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1023\/B:NHAZ.0000007168.00673.27","article-title":"Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability","volume":"30","author":"Lu","year":"2003","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.enggeo.2014.11.008","article-title":"Training Enhanced Reservoir Computing Predictor for Landslide Displacement","volume":"188","author":"Yao","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","article-title":"Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance","volume":"207","author":"Merghadi","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-Induced Landslide Susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_35","first-page":"102713","article-title":"A Hybrid Ensemble-Based Deep-Learning Framework for Landslide Susceptibility Mapping","volume":"108","author":"Lv","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","unstructured":"Dou, J., Xiang, Z., Qiang, X., Zheng, P., Wang, X., Su, A., Liu, J., and Luo, W. (2022). Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation. Earth Sci., (In Chinese)."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Niu, X., Ma, J., Wang, Y., Zhang, J., Chen, H., and Tang, H. (2021). A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction. Appl. Sci., 11.","DOI":"10.3390\/app11104684"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s11063-013-9318-5","article-title":"Landslide Deformation Prediction Based on Recurrent Neural Network","volume":"41","author":"Chen","year":"2015","journal-title":"Neural Process. Lett."},{"key":"ref_39","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_40","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_41","doi-asserted-by":"crossref","unstructured":"Lin, Z., Sun, X., and Ji, Y. (2022). Landslide Displacement Prediction Model Using Time Series Analysis Method and Modified LSTM Model. Electronics, 11.","DOI":"10.3390\/electronics11101519"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1007\/s11440-022-01495-8","article-title":"Displacement Prediction of Jiuxianping Landslide Using Gated Recurrent Unit (GRU) Networks","volume":"17","author":"Zhang","year":"2022","journal-title":"Acta Geotech."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, J., He, Z., Tan, J., and Li, C. (2021). A Novel Displacement Prediction Method Using Gated Recurrent Unit Model with Time Series Analysis in the Erdaohe Landslide, Springer.","DOI":"10.1007\/s11069-020-04337-6"},{"key":"ref_44","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts."},{"key":"ref_45","first-page":"745","article-title":"Advance and Prospects of AdaBoost Algorithm","volume":"39","author":"Ying","year":"2013","journal-title":"Acta Autom. Sin."},{"key":"ref_46","unstructured":"Schapire, R.E. (2003). Nonlinear Estimation and Classification, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, J., Tang, J., and Dai, L.R. (2016, January 8\u201312). RNN-BLSTM Based Multi-Pitch Estimation. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2016, San Francisco, CA, USA.","DOI":"10.21437\/Interspeech.2016-117"},{"key":"ref_48","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_49","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"117240","DOI":"10.1016\/j.eswa.2022.117240","article-title":"Fair-AdaBoost: Extending AdaBoost Method to Achieve Fair Classification","volume":"202","author":"Huang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another Look at Measures of Forecast Accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved Landslide Assessment Using Support Vector Machine with Bagging, Boosting, and Stacking Ensemble Machine Learning Framework in a Mountainous Watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s11069-020-04128-z","article-title":"Mapping the Susceptibility to Landslides Based on the Deep Belief Network: A Case Study in Sichuan Province, China","volume":"103","author":"Wang","year":"2020","journal-title":"Nat. Hazards"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/15\/9\/2296\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:24:16Z","timestamp":1760124256000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/15\/9\/2296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,27]]},"references-count":53,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092296"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/rs15092296","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,27]]}}}