{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T22:55:47Z","timestamp":1777935347368,"version":"3.51.4"},"reference-count":105,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"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":["41807192"],"award-info":[{"award-number":["41807192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70\/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE\u2013RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.<\/jats:p>","DOI":"10.3390\/ijgi9120696","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T20:50:22Z","timestamp":1606251022000},"page":"696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides"],"prefix":"10.3390","volume":"9","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Natural Resources, Xi\u2019an 710075, China"},{"name":"Shaanxi Provincial Land Engineering Construction Group Co. Ltd., Xi\u2019an 710075, China"},{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zenghui","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Natural Resources, Xi\u2019an 710075, China"},{"name":"Shaanxi Provincial Land Engineering Construction Group Co. Ltd., Xi\u2019an 710075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7927-831X","authenticated-orcid":false,"given":"Xinxiang","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-8687","authenticated-orcid":false,"given":"Ataollah","family":"Shirzadi","sequence":"additional","affiliation":[{"name":"Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-6947","authenticated-orcid":false,"given":"Himan","family":"Shahabi","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"},{"name":"Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.geomorph.2015.04.016","article-title":"Large landslides associated with a diapiric fold in canelles reservoir (Spanish pyrenees): Detailed geological-geomorphological mapping, trenching and electrical resistivity imaging","volume":"241","author":"Gutierrez","year":"2015","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.geomorph.2015.02.005","article-title":"Slovenian national landslide database as a basis for statistical assessment of landslide phenomena in Slovenia","volume":"249","author":"Komac","year":"2015","journal-title":"Geomorphology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.geomorph.2006.04.007","article-title":"Estimating the quality of landslide susceptibility models","volume":"81","author":"Guzzetti","year":"2006","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s11069-016-2591-7","article-title":"Landslide susceptibility mapping of the Sera River Basin using logistic regression model","volume":"85","author":"Raja","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s40808-017-0317-9","article-title":"Fuzzy gamma operator model for preparing landslide susceptibility zonation mapping in parts of Kohima Town, Nagaland, India","volume":"3","author":"Sema","year":"2017","journal-title":"Modeling Earth Syst. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, W., Fan, L., Li, C., and Pham, B.T. (2020). Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in Nanzheng county, China. Appl. Sci., 10.","DOI":"10.3390\/app10010029"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2004.06.010","article-title":"The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan","volume":"65","author":"Ayalew","year":"2005","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s11069-016-2434-6","article-title":"Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India","volume":"84","author":"Balamurugan","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Mohammadi, A., Shahabi, H., Ahmad, B.B., Al-Ansari, N., Shirzadi, A., Clague, J.J., Jaafari, A., Chen, W., and Nguyen, H. (2020). Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17144933"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lacasse, S., Nadim, F., Lacasse, S., and Nadim, F. (2009). Landslide Risk Assessment and Mitigation Strategy. Landslides\u2013Disaster Risk Reduction, Springer.","DOI":"10.1007\/978-3-540-69970-5_3"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geoderma.2017.06.020","article-title":"Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques","volume":"305","author":"Chen","year":"2017","journal-title":"Geoderma"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Mandal, S., and Maiti, R. (2015). Application of Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) Model in Assessing Landslide Susceptibility and Risk. Semi-quantitative Approaches for Landslide Assessment and Prediction, Springer.","DOI":"10.1007\/978-981-287-146-6_7"},{"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","first-page":"63","DOI":"10.1007\/s12665-015-4795-7","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China","volume":"75","author":"Chen","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_17","unstructured":"Jie, D., Oguchi, T., Hayakawa, Y.S., Uchiyama, S., Saito, H., and Paudel, U. (2014). GIS-Based Landslide Susceptibility Mapping Using a Certainty Factor Model and Its Validation in the Chuetsu Area, Central Japan. Landslide Science for a Safer Geoenvironment, Springer."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12665-015-4950-1","article-title":"Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran","volume":"75","author":"Pourghasemi","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8647","DOI":"10.1007\/s12665-015-4028-0","article-title":"Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran","volume":"73","author":"Shahabi","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s12303-015-0026-1","article-title":"A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network","volume":"20","author":"Wang","year":"2015","journal-title":"Geosci. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s12303-014-0032-8","article-title":"Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models","volume":"19","author":"Youssef","year":"2014","journal-title":"Geosci. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, G., Lei, X., Chen, W., Shahabi, H., and Shirzadi, A. (2020). Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping. Symmetry, 12.","DOI":"10.3390\/sym12030325"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.geomorph.2013.08.013","article-title":"Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China","volume":"204","author":"Peng","year":"2014","journal-title":"Geomorphology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1007\/s12665-015-5093-0","article-title":"Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China","volume":"75","author":"Chen","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40677-014-0006-1","article-title":"Support vector machine modeling of earthquake-induced landslides susceptibility in central part of Sichuan province, China","volume":"2","author":"Zhou","year":"2015","journal-title":"Geoenviron. Disasters"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Singh, S.K., Al-Ansari, N., Clague, J.J., Jaafari, A., Chen, W., Miraki, S., and Dou, J. (2020). Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Na ve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082749"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12145-018-0354-6","article-title":"Big data in geohazard; pattern mining and large scale analysis of landslides in Iran","volume":"12","author":"Minaei","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s10064-014-0607-7","article-title":"A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece)","volume":"74","author":"Polykretis","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.enggeo.2014.11.014","article-title":"Multiple neural networks switched prediction for landslide displacement","volume":"186","author":"Lian","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8639","DOI":"10.1007\/s12665-015-4027-1","article-title":"Assessing the factor of safety using an artificial neural network: Case studies on landslides in Giresun, Turkey","volume":"73","author":"Gelisli","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"502","DOI":"10.2166\/hydro.2013.191","article-title":"Strategies investigation in using artificial neural network for landslide susceptibility mapping: Application to a Sicilian catchment","volume":"16","author":"Arnone","year":"2014","journal-title":"J. Hydroinform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.ecoleng.2013.07.070","article-title":"Analysis of topographic and vegetative factors with data mining for landslide verification","volume":"61","author":"Tsai","year":"2013","journal-title":"Ecol. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.enggeo.2010.09.009","article-title":"Landslide susceptibility mapping in Injae, Korea, using a decision tree","volume":"116","author":"Yeon","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s10346-014-0466-0","article-title":"A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping","volume":"11","author":"Althuwaynee","year":"2014","journal-title":"Landslides"},{"key":"ref_38","first-page":"175","article-title":"Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study","volume":"10","author":"Cuartero","year":"2012","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., and Chen, W. (2020). Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques. Water, 12.","DOI":"10.3390\/w12010113"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12665-014-3442-z","article-title":"Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets","volume":"73","author":"Park","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s12665-014-3775-7","article-title":"Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios","volume":"73","author":"Kim","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.3390\/e17064271","article-title":"A Hybrid Physical and Maximum-Entropy Landslide Susceptibility Model","volume":"17","author":"Davis","year":"2015","journal-title":"Entropy"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/j.cageo.2010.10.012","article-title":"Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area","volume":"37","author":"Oh","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.gsf.2020.07.012","article-title":"Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer","volume":"12","author":"Chen","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","article-title":"GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods","volume":"196","author":"Chen","year":"2021","journal-title":"CATENA"},{"key":"ref_47","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":"Tuan","year":"2016","journal-title":"Landslides"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2597","DOI":"10.1007\/s10706-017-0264-2","article-title":"Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study","volume":"35","author":"Pham","year":"2017","journal-title":"Geotech. Geol. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1007\/s10706-016-9990-0","article-title":"A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area","volume":"34","author":"Pham","year":"2016","journal-title":"Geotech. Geol. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Zandi, D., Shahabi, H., Chapi, K., Shirzadi, A., Al-Ansari, N., Singh, S.K., Dou, J., and Nguyen, H. (2020). Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran. Appl. Sci., 10.","DOI":"10.3390\/app10155047"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.ecolmodel.2011.12.007","article-title":"How can statistical models help to determine driving factors of landslides?","volume":"239","author":"Vorpahl","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ijsrc.2017.09.008","article-title":"A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India","volume":"33","author":"Pham","year":"2018","journal-title":"Int. J. Sediment Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., Pirasteh, S., Alesheikh, A.A., Khosravi, K., and Panahi, S. (2019). Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Appl. Sci., 9.","DOI":"10.3390\/app9183755"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1080\/01431161.2016.1148282","article-title":"A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison","volume":"37","author":"Althuwaynee","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"743","DOI":"10.3390\/f10090743","article-title":"New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed","volume":"10","author":"Bui","year":"2019","journal-title":"Forests"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.catena.2014.02.005","article-title":"Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia","volume":"118","author":"Umar","year":"2014","journal-title":"Catena"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.catena.2018.01.012","article-title":"GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method","volume":"164","author":"Chen","year":"2018","journal-title":"Catena"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/17445647.2013.852142","article-title":"Landslide inventory map of north-eastern Calabria (South Italy)","volume":"10","author":"Conforti","year":"2014","journal-title":"J. Maps"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104777","DOI":"10.1016\/j.catena.2020.104777","article-title":"GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models","volume":"195","author":"Chen","year":"2020","journal-title":"Catena"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., and Pham, B.T. (2020). Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9070443"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"He, Q., Xu, Z., Li, S., Li, R., Zhang, S., Wang, N., Pham, B.T., and Chen, W. (2019). Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling. Entropy, 21.","DOI":"10.3390\/e21020106"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.catena.2018.12.018","article-title":"Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches","volume":"175","author":"Pham","year":"2019","journal-title":"Catena"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.geomorph.2015.07.012","article-title":"Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China","volume":"248","author":"Guo","year":"2015","journal-title":"Geomorphology"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1007\/s11069-014-1065-z","article-title":"Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeok-ri Creek, South Korea","volume":"72","author":"Pradhan","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modelling: A review of hydrological, geomorphological, and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.2136\/sssaj1986.03615995005000050042x","article-title":"Physical Basis of the Length-slope Factor in the Universal Soil Loss Equation","volume":"50","author":"Moore","year":"1986","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_67","first-page":"127","article-title":"Quantifying the Role of Vegetation in Slope Stability","volume":"4","author":"Sambasivarao","year":"2015","journal-title":"Surg. Neurol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.ecoleng.2009.06.014","article-title":"Quantifying the role of vegetation in slope stability: A case study in Tuscany (Italy)","volume":"36","author":"Schwarz","year":"2010","journal-title":"Ecol. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.geomorph.2013.12.039","article-title":"Scaling properties of rainfall induced landslides predicted by a physically based model","volume":"213","author":"Alvioli","year":"2014","journal-title":"Geomorphol. Amst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1007\/s12665-015-4068-5","article-title":"Research on water-rock (soil) interaction by dynamic tracing method for Huangtupo landslide, Three Gorges Reservoir, PR China","volume":"74","author":"Jiang","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11629-016-4126-9","article-title":"Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China","volume":"14","author":"Du","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s10346-012-0362-4","article-title":"Rainfall-triggered large landslides on 15 December 2005 in Van Canh District, Binh Dinh Province, Vietnam","volume":"10","author":"Duc","year":"2013","journal-title":"Landslides"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1080\/19475705.2016.1255667","article-title":"A novel ensemble classifier of rotation forest and Na\u00efve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS","volume":"8","author":"Pham","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2007.01.003","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations","volume":"72","author":"Yalcin","year":"2008","journal-title":"Catena"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3390\/ijgi9030144","article-title":"Spatial prediction of landslide susceptibility based on gis and discriminant functions","volume":"9","author":"Wang","year":"2020","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s002540050348","article-title":"Landslide zoning in a part of the Garhwal Himalayas","volume":"36","author":"Pachauri","year":"1998","journal-title":"Environ. Geol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/0025-5564(75)90047-4","article-title":"A model of inexact reasoning in medicine","volume":"23","author":"Shortliffe","year":"1990","journal-title":"Math. Biosci."},{"key":"ref_78","first-page":"167","article-title":"Probabilistic Interpretations for MYCIN\u2019s Certainty Factors","volume":"Volume 4","author":"Heckerman","year":"1990","journal-title":"Machine Intelligence and Pattern Recognition"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.enggeo.2004.06.009","article-title":"Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China","volume":"76","author":"Lan","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_80","unstructured":"Bonhamcarter, G. (1994). Geographic Information Systems for Geoscientists: Modelling with GIS, Computer Methods in the Geosciences; Elsevier."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.catena.2011.01.014","article-title":"A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey","volume":"85","author":"Yalcin","year":"2011","journal-title":"Catena"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.geomorph.2008.05.041","article-title":"Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence","volume":"102","author":"Dahal","year":"2008","journal-title":"Geomo"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A Theory of Statistical Evidence. Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, Springer.","DOI":"10.1007\/978-94-010-1436-6_11"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2012.03.003","article-title":"Application of an evidential belief function model in landslide susceptibility mapping","volume":"44","author":"Althuwaynee","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.catena.2012.07.014","article-title":"Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea","volume":"100","author":"Lee","year":"2013","journal-title":"Catena"},{"key":"ref_86","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_87","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.eswa.2014.07.042","article-title":"Indoor localization in a hospital environment using Random Forest classifiers","volume":"42","author":"Calderoni","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0378-1127(02)00475-9","article-title":"Sustainability and forest soils","volume":"171","author":"Khanna","year":"2002","journal-title":"Forest Ecol. Manag."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cmpb.2016.03.020","article-title":"Congestive heart failure detection using random forest classifier","volume":"130","author":"Masetic","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geomorph.2017.09.007","article-title":"Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques","volume":"297","author":"Chen","year":"2017","journal-title":"Geomorphology"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s12040-013-0282-2","article-title":"Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran","volume":"122","author":"Pourghasemi","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). Controlling the Generalization Ability of Learning Processes. The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/s12517-017-2918-z","article-title":"Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping","volume":"10","author":"Feizizadeh","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_94","unstructured":"Frank, E., Hall, A.M., and Witten, H.I. (2016). The Weka Workbench. Online Appendix for \"Data Mining: Practical Machine Learning Tools and Techniques\", Morgan Kaufmann. [4th ed.]."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4397","DOI":"10.1007\/s10064-018-1401-8","article-title":"Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling","volume":"78","author":"Chen","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., Avand, M., Janizadeh, S., Kariminejad, N., Shahabi, H., Costache, R., Shahabi, H., Shirzadi, A., and Mosavi, A. (2020). GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran. Remote Sens., 12.","DOI":"10.3390\/rs12152478"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques. Appl. Sci., 10.","DOI":"10.3390\/app10010016"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Chen, W., Clague, J.J., Geertsema, M., Jaafari, A., Avand, M., Miraki, S., and Talebpour Asl, D. (2020). Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests, 11.","DOI":"10.3390\/f11040421"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1007\/s12665-019-8562-z","article-title":"A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling","volume":"78","author":"Abedini","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"124602","DOI":"10.1016\/j.jhydrol.2020.124602","article-title":"Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping","volume":"583","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Mohammadi, A., Shahabi, H., Ahmad, B.B., Al-Ansari, N., Shirzadi, A., Geertsema, M., Kress, V.R., Karimzadeh, S., and Kamran, K.V. (2020). Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms. Forests, 11.","DOI":"10.3390\/f11080830"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00704-015-1702-9","article-title":"Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of na\u00efve bayes, multilayer perceptron neural networks, and functional trees methods","volume":"128","author":"Pham","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Nguyen, V., Pham, B., Vu, B., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D., Kumar, R., and Chatterjee, J. (2019). Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling. Forests, 10.","DOI":"10.3390\/f10020157"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Shahabi, H., Omidvar, E., Shirzadi, A., Geertsema, M., Clague, J., Khosravi, K., Pradhan, B., Pham, B., and Chapi, K. (2019). Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11080931"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1007\/s10064-018-1281-y","article-title":"A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling","volume":"78","author":"Pham","year":"2019","journal-title":"Bull. Eng. Geol. Environ."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:53Z","timestamp":1760179013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,24]]},"references-count":105,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["ijgi9120696"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9120696","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,24]]}}}