{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T12:55:15Z","timestamp":1780923315707,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1711267"],"award-info":[{"award-number":["U1711267"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018555","name":"Science and Technology Program of Guizhou Province","doi-asserted-by":"publisher","award":["[2020]4Y039"],"award-info":[{"award-number":["[2020]4Y039"]}],"id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project Funding of Investigation and Evaluation of Guizhou Provincial Geological 3D Spatial Strategy","award":["2019-02"],"award-info":[{"award-number":["2019-02"]}]},{"name":"Geological Scientific Research Project of Geology and Mineral Exploration and Development Bureau Guizhou Province","award":["[2018]07"],"award-info":[{"award-number":["[2018]07"]}]},{"name":"Open research project of key laboratory of Tectonics and Petroleum Resources, Ministry of Education","award":["TPR-2019-11"],"award-info":[{"award-number":["TPR-2019-11"]}]},{"name":"Open fund project of National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns","award":["CTCZ19K01"],"award-info":[{"award-number":["CTCZ19K01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.<\/jats:p>","DOI":"10.3390\/rs13183573","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T10:12:03Z","timestamp":1631095923000},"page":"3573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3951-1548","authenticated-orcid":false,"given":"Chunfang","family":"Kong","sequence":"first","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"},{"name":"Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China"},{"name":"National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang 421000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3552-339X","authenticated-orcid":false,"given":"Yiping","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"},{"name":"Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9110-7369","authenticated-orcid":false,"given":"Xiaogang","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Idaho, Moscow, ID 83844, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengping","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"},{"name":"Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"},{"name":"Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8967-9173","authenticated-orcid":false,"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer, China University of Geosciences, Wuhan 430074, China"},{"name":"Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang 550081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.catena.2012.05.005","article-title":"Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran","volume":"97","author":"Pourghasemi","year":"2012","journal-title":"Catena"},{"key":"ref_2","unstructured":"Huang, Z., and He, W. (2018). The Field Investigation Report of the Geological Hazards Project by Guangxi Geological Survey Bureau, Guangxi Geological Survey Bureau Office."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s00477-019-01671-5","article-title":"Conditional multiple-point geostatistical simulation for unevenly distributed sample data","volume":"33","author":"Chen","year":"2019","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_4","first-page":"64","article-title":"spatial-temporal distribution characteristics and genetic analysis of geological disasters in Guangxi","volume":"6","author":"Zhang","year":"2016","journal-title":"Guangxi Water Resour. Hydropower Eng."},{"key":"ref_5","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":"2017","journal-title":"Comput. Geosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s11069-015-2075-1","article-title":"Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)","volume":"81","author":"Myronidis","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1007\/s10064-018-1259-9","article-title":"A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India","volume":"78","author":"Sharma","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10346-018-1072-3","article-title":"Landslide susceptibility assessment by TRIGRS in a frequently affected shallow instability area","volume":"16","author":"Ciurleo","year":"2019","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12517-012-0807-z","article-title":"Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya","volume":"7","author":"Regmi","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_10","first-page":"94","article-title":"Risk Assessment on karst collapse of the highway subgrade based on weights of evidence method","volume":"30","author":"Sun","year":"2019","journal-title":"Chin. J. Geol. Hazards Control."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, L., and Lan, H. (2020). Integration of spatial probability and size in slope-unit-based landslide susceptibility assessment: A case study. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17218055"},{"key":"ref_13","first-page":"1699","article-title":"Geological disaster susceptibility evaluation based on certainty factor and support vector machine","volume":"20","author":"Li","year":"2018","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_14","first-page":"1153","article-title":"Assessment of regional landslide susceptibility based on combined model of certainty factor method","volume":"27","author":"Yang","year":"2019","journal-title":"J. Eng. Geol."},{"key":"ref_15","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_16","first-page":"243","article-title":"Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Int. J. Environ. Res","volume":"15","author":"Jean","year":"2018","journal-title":"Public Health"},{"key":"ref_17","unstructured":"Huang, R., Xu, X., Tang, C., and Xiang, X. (2008). Geological Environmental Assessment and Geological Hazard Management, Science Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.enggeo.2011.09.006","article-title":"Landslide susceptibility assessment using SVM machine learning algorithm","volume":"123","author":"Bajat","year":"2011","journal-title":"Eng. Geol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"974638","DOI":"10.1155\/2012\/974638","article-title":"Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and na\u00efve bayes models","volume":"2012","author":"Bui","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Bui","year":"2016","journal-title":"Landslides"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"104329","DOI":"10.1016\/j.cageo.2019.104329","article-title":"Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling","volume":"133","author":"Aktas","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_23","first-page":"62","article-title":"Assessment of landslide susceptibility based on SVM-LR model: A case study of Lintong District","volume":"19","author":"Wang","year":"2019","journal-title":"Sci. Technol. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s11053-020-09710-7","article-title":"A novel combination of whale optimization algorithm and support vector machine with different kernel functions for prediction of blasting-induced fly-rock in quarry mines","volume":"30","author":"Nguyen","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.3390\/rs70809705","article-title":"Identification of forested landslides using lidar data, object-based image analysis, and machine learning algorithms","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.cageo.2011.09.011","article-title":"Susceptibility assessment of earthquake-induced landslides using bayesian network: A case study in Beichuan, China","volume":"42","author":"Song","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.envsoft.2016.07.005","article-title":"A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)","volume":"84","author":"Pham","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1080\/13658816.2014.992436","article-title":"Landslide susceptibility evaluation based on BPNN and GIS: A case of Guojiaba in the three gorges reservoir area","volume":"29","author":"Xu","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11069-017-3043-8","article-title":"Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey","volume":"90","author":"Ada","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Brenning, A. (2021). Active-learning approaches for landslide mapping using support vector machines. Remote Sens., 13.","DOI":"10.3390\/rs13132588"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sevgen, E., Kocaman, S., Nefeslioglu, H.A., and Gokceoglu, C. (2019). A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors, 19.","DOI":"10.3390\/s19183940"},{"key":"ref_34","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_35","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_36","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide susceptibility mapping using J48 Decision Tree with Adaboost, Bagging and Rotation Forest ensembles in the Guangchang area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"Catena"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","article-title":"A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran","volume":"627","author":"Khosravi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rse.2014.07.004","article-title":"Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China","volume":"152","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_40","first-page":"2800","article-title":"Random forest method for predicting coal spontaneous combustion in gob","volume":"43","author":"Deng","year":"2018","journal-title":"J. China Coal Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107201","DOI":"10.1016\/j.geomorph.2020.107201","article-title":"A random forest model of landslide susceptibility mapping based on hyper-parameter optimization using Bayes algorithm","volume":"362","author":"Sun","year":"2020","journal-title":"Geomorphology"},{"key":"ref_42","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_43","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_44","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/S0167-8655(99)00087-2","article-title":"Support vector domain description","volume":"20","author":"Tax","year":"1999","journal-title":"Pattern Recogn. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s12665-017-6731-5","article-title":"The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir Area, China","volume":"76","author":"Zhang","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.enggeo.2009.12.004","article-title":"Techniques for evaluating the performance of landslide susceptibility models","volume":"111","author":"Frattini","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","article-title":"A method of comparing the areas under receiver operating characteristic curves derived from the same cases","volume":"148","author":"Hanley","year":"1983","journal-title":"Radiology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2005","journal-title":"Pattern Recogn. Lett."},{"key":"ref_51","first-page":"170","article-title":"Landslide susceptibility assessment based on PSO-BP neural network","volume":"42","author":"Feng","year":"2017","journal-title":"Sci. Surv. Mapp."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3573\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:58:49Z","timestamp":1760165929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3573"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":51,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183573"],"URL":"https:\/\/doi.org\/10.3390\/rs13183573","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,8]]}}}