{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:06:16Z","timestamp":1775736376632,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility evaluation (LSE) refers to the probability of landslide occurrence in a region under a specific geological environment and trigger conditions, which is crucial to preventing and controlling landslide risk. The mainstream of the Yangtze River in Yichang City belongs to the largest basin in the Three Gorges Reservoir area and is prone to landslides. Affected by global climate change, seismic activity, and accelerated urbanization, geological disasters such as landslide collapses and debris flows in the study area have increased significantly. Therefore, it is urgent to carry out the LSE in the Yichang section of the Yangtze River Basin. The main results are as follows: (1) Based on historical landslide catalog, geological data, geographic data, hydrological data, remote sensing data, and other multi-source spatial-temporal big data, we construct the LSE index system; (2) In this paper, unsupervised Deep Embedding Clustering (DEC) algorithm and deep integration network (Capsule Neural Network based on SENet: SE-CapNet) are used for the first time to participate in non-landslide sample selection, and LSE in the study area and the accuracy of the algorithm is 96.29; (3) Based on the constructed sensitivity model and rainfall forecast data, the main driving mechanisms of landslides in the Yangtze River Basin were revealed. In this paper, the study area\u2019s mid-long term LSE prediction and trend analysis are carried out. (4) The complete results show that the method has good performance and high precision, providing a reference for subsequent LSE, landslide susceptibility prediction (LSP), and change rule research, and providing a scientific basis for landslide disaster prevention.<\/jats:p>","DOI":"10.3390\/rs14112717","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T00:10:33Z","timestamp":1654560633000},"page":"2717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Evaluation and Prediction of Landslide Susceptibility in Yichang Section of Yangtze River Basin Based on Integrated Deep Learning Algorithm"],"prefix":"10.3390","volume":"14","author":[{"given":"Lili","family":"Chang","sequence":"first","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430079, China"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People\u2019s Republic of China, Beijing 100048, China"}]},{"given":"Chunsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. 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