{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T01:33:56Z","timestamp":1783042436598,"version":"3.54.6"},"reference-count":68,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFC1509401"],"award-info":[{"award-number":["2019YFC1509401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Landslide susceptibility mapping (LSM) is of great significance for the identification and prevention of geological hazards. LSM is based on convolutional neural networks (CNNs); CNNs use fixed convolutional kernels, focus more on local information and do not retain spatial information. This is a property of the CNN itself, resulting in low accuracy of LSM. Based on the above problems, we use Vision Transformer (ViT) and its derivative model Swin Transformer (Swin) to conduct LSM for the selected study area. Machine learning and a CNN model are used for comparison. Fourier transform amplitude, feature similarity and other indicators were used to compare and analyze the difference in the results. The results show that the Swin model has the best accuracy, F1-score and AUC. The results of LSM are combined with landslide points, faults and other data analysis; the ViT model results are the most consistent with the actual situation, showing the strongest generalization ability. In this paper, we believe that the advantages of ViT and its derived models in global feature extraction ensure that ViT is more accurate than CNN and machine learning in predicting landslide probability in the study area.<\/jats:p>","DOI":"10.3390\/s22239104","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T03:58:16Z","timestamp":1669262296000},"page":"9104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Application of Transformer Models to Landslide Susceptibility Mapping"],"prefix":"10.3390","volume":"22","author":[{"given":"Shuai","family":"Bao","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5519-934X","authenticated-orcid":false,"given":"Xizhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1139\/t94-031","article-title":"Landslide risk assessment and acceptable risk","volume":"31","author":"Fell","year":"1994","journal-title":"Can. Geotech. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/BF02910325","article-title":"Visualization of 3-D digital elevation model for landslide assessment and prediction in mountainous terrain: A case study of Chandmari landslide, Sikkim, eastern Himalayas","volume":"9","author":"Dubey","year":"2005","journal-title":"Geosci. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/srep09899","article-title":"Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment","volume":"5","author":"Shahabi","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gantimurova, S., Parshin, A., and Erofeev, V. (2021). GIS-Based Landslide Susceptibility Mapping of the Circum-Baikal Railway in Russia Using UAV Data. Remote Sens., 13.","DOI":"10.3390\/rs13183629"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11042","DOI":"10.1109\/JSTARS.2021.3122825","article-title":"Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region","volume":"14","author":"Xiong","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adnan, M., Rahman, M.S., Ahmed, N., Ahmed, B., and Rahman, R.M. (2020). Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12203347"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104470","DOI":"10.1016\/j.cageo.2020.104470","article-title":"Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping","volume":"139","author":"Fang","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.scitotenv.2017.02.188","article-title":"Mapping landslide susceptibility using data-driven methods","volume":"589","author":"Pereira","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cageo.2011.05.010","article-title":"Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)","volume":"38","author":"Aksoy","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10346-005-0031-y","article-title":"A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method","volume":"3","author":"Yoshimatsu","year":"2006","journal-title":"Landslides"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation. Remote Sens., 12.","DOI":"10.3390\/rs12142180"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cageo.2016.10.001","article-title":"An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software","volume":"98","author":"Sezer","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1061\/(ASCE)1090-0241(2003)129:12(1109)","article-title":"Geographic Information Systems-Based Three-Dimensional Critical Slope Stability Analysis and Landslide Hazard Assessment","volume":"129","author":"Xie","year":"2003","journal-title":"J. Geotech. Geoenviron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11069-012-0217-2","article-title":"Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran","volume":"63","author":"Pourghasemi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Didehban, K., Rasouli, H., Kamran, K., Feizizadeh, B., and Blaschke, T. (2020). An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100561"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.1007\/s12517-014-1369-z","article-title":"Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China","volume":"7","author":"Chen","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.gsf.2020.09.004","article-title":"GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms","volume":"12","author":"Saa","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2015.04.007","article-title":"Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling","volume":"81","author":"Goetz","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.gsf.2020.06.013","article-title":"Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran","volume":"12","author":"Ngo","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nachappa, T., Ghorbanzadeh, O., Gholamnia, K., and Blaschke, T. (2020). Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sens., 12.","DOI":"10.3390\/rs12172757"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide detection using deep learning and object-based image analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s10346-010-0202-3","article-title":"Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model","volume":"7","author":"Chauhan","year":"2010","journal-title":"Landslides"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s11069-015-1915-3","article-title":"Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: Application to the 2009 storm event in Messina (Sicily, southern Italy)","volume":"79","author":"Lombardo","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.enggeo.2016.02.009","article-title":"Application of time series analysis and PSO\u2013SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China","volume":"204","author":"Zhou","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.geomorph.2008.02.011","article-title":"Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China","volume":"101","author":"Yao","year":"2008","journal-title":"Geomorphology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2015.06.001","article-title":"Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)","volume":"249","author":"Trigila","year":"2015","journal-title":"Geomorphology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1007\/s12517-019-4892-0","article-title":"Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN)","volume":"12","author":"Harmouzi","year":"2019","journal-title":"Arab. J. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"14629","DOI":"10.1038\/s41598-021-94190-9","article-title":"A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)","volume":"11","author":"Ghorbanzadeh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3625","DOI":"10.1109\/JSTARS.2021.3066378","article-title":"Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24112","DOI":"10.1038\/s41598-021-03585-1","article-title":"Deep learning-based landslide susceptibility mapping","volume":"11","author":"Azarafza","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_34","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_35","first-page":"102508","article-title":"A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping","volume":"104","author":"He","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/JSTARS.2020.3043836","article-title":"Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network (CNN) Streams Combined by the Dempster\u2014Shafer (DS) model","volume":"14","author":"Ghorbanzadeh","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104445","DOI":"10.1016\/j.cageo.2020.104445","article-title":"Comparative study of landslide susceptibility mapping with different recurrent neural networks","volume":"138","author":"Wang","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.scitotenv.2019.02.263","article-title":"Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China","volume":"666","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Rahimzad, M., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Homayouni, S., Blaschke, T., Lim, S., and Ghamisi, P. (2021). Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224698"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2017.04.013","article-title":"Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine","volume":"223","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1080\/19479832.2021.1961316","article-title":"Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province","volume":"12","author":"Liu","year":"2021","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10346-013-0391-7","article-title":"Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression","volume":"11","author":"Kavzoglu","year":"2014","journal-title":"Landslides"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Brousseau, B., Rose, J., and Eizenman, M. (2020). Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model. Sensors, 20.","DOI":"10.3390\/s20020543"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.neucom.2019.11.012","article-title":"A Semi-Supervised Laplacian Extreme Learning Machine and Feature Fusion with CNN for Industrial Superheat Identification","volume":"381","author":"Lei","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3092818","article-title":"Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors","volume":"36","author":"Chu","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"12133","DOI":"10.1007\/s11042-016-4142-3","article-title":"Query expansion for object retrieval with active learning using BoW and CNN feature","volume":"76","author":"Zhao","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_47","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_48","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., and Houlsby, N. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv Preprint."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2250009","DOI":"10.1142\/S1793545822500092","article-title":"Computer-aided diagnosis of retinopathy based on vision transformer","volume":"15","author":"Jiang","year":"2022","journal-title":"J. Innov. Opt. Health Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery","volume":"13","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.5194\/essd-12-1625-2020","article-title":"Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform","volume":"12","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111395","DOI":"10.1016\/j.rse.2019.111395","article-title":"Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China","volume":"233","author":"Yang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A Caution Regarding Rules of Thumb for Variance Inflation Factors","volume":"41","year":"2007","journal-title":"Qual. Quant."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101203","DOI":"10.1016\/j.gsf.2021.101203","article-title":"Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India","volume":"12","author":"Km","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yu, L., Cao, Y., Zhou, C., Wang, Y., and Huo, Z. (2019). Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines: A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China. Appl. Sci., 9.","DOI":"10.3390\/app9224756"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_58","first-page":"9110866","article-title":"Research on Maize Disease Recognition Method Based on Improved ResNet50","volume":"2021","author":"Wang","year":"2021","journal-title":"Mob. Inf. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.gsf.2021.101211","article-title":"Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization","volume":"12","author":"Zhou","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"105572","DOI":"10.1016\/j.enggeo.2020.105572","article-title":"Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas","volume":"270","author":"Du","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_62","unstructured":"Park, N., and Kim, S. (2022). How do Vision Transformers Work?. arXiv."},{"key":"ref_63","first-page":"12116","article-title":"Do Vision Transformers See Like Convolutional Neural Networks?","volume":"34","author":"Raghu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5100","DOI":"10.3390\/rs13245100","article-title":"Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images","volume":"13","author":"Vateekul","year":"2021","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106103","DOI":"10.1016\/j.enggeo.2021.106103","article-title":"AI-powered Landslide Susceptibility Assessment in Hong Kong","volume":"288","author":"Hw","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2017.11.019","article-title":"Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China","volume":"112","author":"Zhou","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105147","DOI":"10.1016\/j.enggeo.2019.105147","article-title":"GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China","volume":"259","author":"Yu","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.enggeo.2019.02.004","article-title":"A novel physically-based model for updating landslide susceptibility","volume":"251","author":"Wang","year":"2019","journal-title":"Eng. Geol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:29Z","timestamp":1760145929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,23]]},"references-count":68,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239104"],"URL":"https:\/\/doi.org\/10.3390\/s22239104","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,23]]}}}