{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:42:59Z","timestamp":1768689779676,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX20_0484"],"award-info":[{"award-number":["KYCX20_0484"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["B200203105"],"award-info":[{"award-number":["B200203105"]}]},{"name":"Fundamental Research Funds for the Central Universi-ties","award":["KYCX20_0484"],"award-info":[{"award-number":["KYCX20_0484"]}]},{"name":"Fundamental Research Funds for the Central Universi-ties","award":["B200203105"],"award-info":[{"award-number":["B200203105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model.<\/jats:p>","DOI":"10.3390\/rs15082149","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T01:42:39Z","timestamp":1681954959000},"page":"2149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Research on the Uncertainty of Landslide Susceptibility Prediction Using Various Data-Driven Models and Attribute Interval Division"],"prefix":"10.3390","volume":"15","author":[{"given":"Yin","family":"Xing","sequence":"first","affiliation":[{"name":"School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Technology, Suzhou Institute of Trade & Commerce, Suzhou 215009, China"}]},{"given":"Saipeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China"}]},{"given":"Wei","family":"Xie","sequence":"additional","affiliation":[{"name":"Quanzhou Equipment Manufacturing Research Center, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0669-6219","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}]},{"given":"Yunfei","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1007\/s10346-020-01473-9","article-title":"Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103262","DOI":"10.1016\/j.compgeo.2019.103262","article-title":"A methodology for uncertainty analysis of landslides triggered by an earthquake","volume":"117","author":"Khalaj","year":"2020","journal-title":"Comput. 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