{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:19:32Z","timestamp":1773213572720,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:00:00Z","timestamp":1613779200000},"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":["41702327"],"award-info":[{"award-number":["41702327"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51874268"],"award-info":[{"award-number":["51874268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.<\/jats:p>","DOI":"10.3390\/ijgi10020093","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T21:15:01Z","timestamp":1613942101000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5689-2157","authenticated-orcid":false,"given":"Wei","family":"Xie","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"},{"name":"School of Earth Sciences and Technology, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoshuang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"},{"name":"College of Civil Engineering and Architecture, Guangxi University of Science and Technology, Liuzhou 545006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Jian","sequence":"additional","affiliation":[{"name":"Department of Geotechnical and Geological Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Technology, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geotechnical and Geological Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis F.","family":"Robledo","sequence":"additional","affiliation":[{"name":"Engineering Science Department, Universidad Andres Bello, Santiago 7500971, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"},{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, 362000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.earscirev.2016.08.011","article-title":"Landslides in a changing climate","volume":"162","author":"Gariano","year":"2016","journal-title":"Earth Sci. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1007\/s00704-018-2673-4","article-title":"New insights in the relation between climate and slope failures at high-elevation sites","volume":"137","author":"Paranunzio","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1029\/2018RG000626","article-title":"Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts","volume":"57","author":"Fan","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s10346-018-1037-6","article-title":"Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016","volume":"15","author":"Lin","year":"2018","journal-title":"Landslides"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1007\/s00254-005-1228-z","article-title":"Probabilistic landslide susceptibility and factor effect analysis","volume":"47","author":"Lee","year":"2005","journal-title":"Environ. Geol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10346-014-0550-5","article-title":"A systematic review of landslide probability mapping using logistic regression","volume":"12","author":"Budimir","year":"2015","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.enggeo.2019.05.007","article-title":"Machine learning based landslide assessment of the Belgrade metropolitan area: Pixel resolution effects and a cross-scaling concept","volume":"256","author":"Abolmasov","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.geomorph.2017.12.007","article-title":"Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods","volume":"303","author":"Lee","year":"2018","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"853","DOI":"10.5194\/nhess-5-853-2005","article-title":"Spatial prediction models for landslide hazards: Review, comparison and evaluation","volume":"5","author":"Brenning","year":"2005","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s10346-015-0576-3","article-title":"Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map","volume":"13","author":"Ilia","year":"2016","journal-title":"Landslides"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, Q., and Liu, Y. (2020). Mapping landslide susceptibility using machine learning algorithms and GIS: A case study in Shexian county, Anhui province, China. Symmetry, 12.","DOI":"10.3390\/sym12121954"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s00267-003-0077-3","article-title":"Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS","volume":"34","author":"Lee","year":"2004","journal-title":"Environ. Manag."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s00366-018-0644-0","article-title":"Modification of landslide susceptibility mapping using optimized PSO-ANN technique","volume":"35","author":"Moayedi","year":"2019","journal-title":"Eng. Comput."},{"key":"ref_19","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_20","first-page":"1367","article-title":"Improving predictive power of physically based rainfall-induced shallow landslide models: A probabilistic approach","volume":"6","author":"Raia","year":"2013","journal-title":"Geosci. Model Dev. Discuss."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1007\/s10346-019-01174-y","article-title":"Local-scale landslide susceptibility mapping using the B-GeoSVC model","volume":"16","author":"Yang","year":"2019","journal-title":"Landslides"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xiao, L., Zhang, Y., and Peng, G. (2018). Landslide susceptibility assessment using integrated deep learning algorithm along the china-nepal highway. Sensors, 18.","DOI":"10.3390\/s18124436"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-018-3531-5","article-title":"Analysis and evaluation of landslide susceptibility: A review on articles published during 2005\u20132016 (periods of 2005\u20132012 and 2013\u20132016)","volume":"11","author":"Pourghasemi","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0169-555X(99)00042-2","article-title":"A view on some hydrological triggering systems in landslides","volume":"30","author":"Buma","year":"1999","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The Varnes classification of landslide types, an update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2014.05.013","article-title":"Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale","volume":"152","author":"Jebur","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1080\/10106049.2019.1588393","article-title":"Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree","volume":"34","author":"Chen","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00704-018-2628-9","article-title":"A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data","volume":"137","author":"Tehrany","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Lu, Z. (2018). Remote sensing of landslides-A review. Remote Sens., 10.","DOI":"10.3390\/rs10020279"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s10346-018-01122-2","article-title":"An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data","volume":"16","author":"Liu","year":"2019","journal-title":"Landslides"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s11069-016-2725-y","article-title":"Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Ro\u017cn\u00f3w Lake, Poland","volume":"86","author":"Pawluszek","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_34","unstructured":"Weiss, A.D. (2001, January 9\u201313). Topographic position and landforms analysis. Proceedings of the ESRI User Conference, San Diego, CA, USA. Available online: http:\/\/www.jennessent.com\/downloads\/tpi-poster-tnc_18x22.pdf."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.geomorph.2005.07.018","article-title":"Changes in land cover and shallow landslide activity: A case study in the Spanish Pyrenees","volume":"74","year":"2006","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1007\/s10346-019-01178-8","article-title":"Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide","volume":"16","author":"Yang","year":"2019","journal-title":"Landslides"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/S1002-0160(17)60294-7","article-title":"Slope Processes, Mass Movement and Soil Erosion: A Review","volume":"27","author":"Guerra","year":"2017","journal-title":"Pedosphere"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.earscirev.2018.02.013","article-title":"Territorial early warning systems for rainfall-induced landslides","volume":"179","author":"Piciullo","year":"2018","journal-title":"Earth Sci. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/10106049.2017.1323964","article-title":"Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea","volume":"33","author":"Kim","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/20964471.2018.1472392","article-title":"Mapping landslide susceptibility and types using Random Forest","volume":"2","author":"Taalab","year":"2018","journal-title":"Big Earth Data"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s11442-016-1331-y","article-title":"Human activity intensity of land surface: Concept, methods and application in China","volume":"26","author":"Xu","year":"2016","journal-title":"J. Geogr. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/j.scitotenv.2018.04.085","article-title":"Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors","volume":"634","author":"Chi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ecolind.2016.02.052","article-title":"A measure of spatial stratified heterogeneity","volume":"67","author":"Wang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/13658810802443457","article-title":"Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China","volume":"24","author":"Wang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.geomorph.2018.09.019","article-title":"New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China","volume":"324","author":"Yang","year":"2019","journal-title":"Geomorphology"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1080\/13658816.2016.1165228","article-title":"Driving forces and their interactions of built-up land expansion based on the geographical detector\u2014A case study of Beijing, China","volume":"30","author":"Ju","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.jclepro.2019.05.342","article-title":"Quantifying the spatial heterogeneity influences of natural and socioeconomic factors and their interactions on air pollution using the geographical detector method: A case study of the Yangtze River Economic Belt, China","volume":"232","author":"Bai","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102064","DOI":"10.1016\/j.habitatint.2019.102064","article-title":"Spatial determinants of urban wet market vendor profit in Nanjing, China","volume":"94","author":"Qi","year":"2019","journal-title":"Habitat Int."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.envsoft.2012.01.015","article-title":"Environmental health risk detection with GeogDetector","volume":"33","author":"Wang","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_51","first-page":"1","article-title":"An Evolutionary Algorithm for Automated Machine Learning Focusing on Classifier Ensembles: An improved algorithm and extended results","volume":"805","author":"Freitas","year":"2019","journal-title":"Theor. Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101822","DOI":"10.1016\/j.artmed.2020.101822","article-title":"Arti fi cial Intelligence in Medicine Automated machine learning: Review of the state-of-the-art and opportunities for healthcare","volume":"104","author":"Waring","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s12665-009-0426-5","article-title":"Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya","volume":"61","author":"Poudyal","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"104225","DOI":"10.1016\/j.catena.2019.104225","article-title":"Landslide susceptibility hazard map in southwest Sweden using artificial neural network","volume":"183","author":"Spross","year":"2019","journal-title":"Catena"},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1080\/10106049.2019.1585482","article-title":"Landslide susceptibility mapping using Na\u00efve Bayes and Bayesian network models in Umyeonsan, Korea","volume":"35","author":"Lee","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11069-005-5182-6","article-title":"Validation and evaluation of predictive models in hazard assessment and risk management","volume":"37","year":"2006","journal-title":"Nat. Hazards"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.geomorph.2018.04.010","article-title":"GIS-based evaluation of diagnostic areas in landslide susceptibility analysis of Bahluie\u021b River Basin (Moldavian Plateau, NE Romania). Are Neolithic sites in danger?","volume":"314","author":"Nicu","year":"2018","journal-title":"Geomorphology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1007\/s11069-014-1532-6","article-title":"A method to reveal climatic variables triggering slope failures at high elevation","volume":"76","author":"Paranunzio","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6323","DOI":"10.3390\/su11226323","article-title":"A novel intelligence approach of a sequential minimal optimization-based support vector machine for landslide susceptibility mapping","volume":"11","author":"Pham","year":"2019","journal-title":"Sustainability"},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.catena.2017.11.022","article-title":"Prediction of the landslide susceptibility: Which algorithm, which precision?","volume":"162","author":"Pourghasemi","year":"2018","journal-title":"Catena"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/2\/93\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:26:45Z","timestamp":1760160405000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/2\/93"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,20]]},"references-count":63,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["ijgi10020093"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10020093","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,20]]}}}