{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:26:43Z","timestamp":1774880803309,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Bureau of Geology and mineral resources exploration and development of Sichuan Province","award":["20170612-0413"],"award-info":[{"award-number":["20170612-0413"]}]},{"DOI":"10.13039\/501100004613","name":"China Geological Survey","doi-asserted-by":"publisher","award":["1512007402438"],"award-info":[{"award-number":["1512007402438"]}],"id":[{"id":"10.13039\/501100004613","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.<\/jats:p>","DOI":"10.3390\/rs13112166","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T23:33:33Z","timestamp":1622504013000},"page":"2166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4599-530X","authenticated-orcid":false,"given":"Xin","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0310-8044","authenticated-orcid":false,"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Mei","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Jingjue","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Earth Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2822-9336","authenticated-orcid":false,"given":"Tianqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yuantao","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology & Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Yuting","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.catena.2016.06.004","article-title":"Comparison of a logistic regression and Na\u00efve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size","volume":"145","author":"Tsangaratos","year":"2016","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hassangavyar, M.B., Damaneh, H.E., Pham, Q.B., Linh, N.T.T., Tiefenbacher, J., and Bach, Q.-V. 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