{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T08:13:19Z","timestamp":1784448799633,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFB3903604"],"award-info":[{"award-number":["2022YFB3903604"]}]},{"name":"National Key R&amp;D Program of China","award":["DD20211364"],"award-info":[{"award-number":["DD20211364"]}]},{"name":"National Key R&amp;D Program of China","award":["2652023001"],"award-info":[{"award-number":["2652023001"]}]},{"name":"China Geological Survey \u201cLandslide monitoring technology and intelligent early warning application demonstration\u201d","award":["2022YFB3903604"],"award-info":[{"award-number":["2022YFB3903604"]}]},{"name":"China Geological Survey \u201cLandslide monitoring technology and intelligent early warning application demonstration\u201d","award":["DD20211364"],"award-info":[{"award-number":["DD20211364"]}]},{"name":"China Geological Survey \u201cLandslide monitoring technology and intelligent early warning application demonstration\u201d","award":["2652023001"],"award-info":[{"award-number":["2652023001"]}]},{"name":"\u201cDeep-time Digital Earth\u201d Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing)","award":["2022YFB3903604"],"award-info":[{"award-number":["2022YFB3903604"]}]},{"name":"\u201cDeep-time Digital Earth\u201d Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing)","award":["DD20211364"],"award-info":[{"award-number":["DD20211364"]}]},{"name":"\u201cDeep-time Digital Earth\u201d Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing)","award":["2652023001"],"award-info":[{"award-number":["2652023001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.<\/jats:p>","DOI":"10.3390\/rs16030566","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T09:43:22Z","timestamp":1706780602000},"page":"566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-7399","authenticated-orcid":false,"given":"Dongping","family":"Ming","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China"},{"name":"Frontiers Science Center for Deep-Time Digital Earth, China University of Geosciences Beijing, Beijing 100083, China"},{"name":"Key Laboratory of Intraplate Volcanoes and Earthquakes, China University of Geosciences Beijing, Ministry of Education, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3405-3317","authenticated-orcid":false,"given":"Yunyun","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangyang","family":"Chen","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107228","DOI":"10.1016\/j.geomorph.2020.107228","article-title":"Spatial and Temporal Evolution of Co-Seismic Landslides after the 2005 Kashmir Earthquake","volume":"362","author":"Shafique","year":"2020","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Ahmadi, R., Alizadeh, M., Hashim, M., Al-Ansari, N., Shirzadi, A., Wolf, I.D., and Ariffin, E.H. 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