{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:19:32Z","timestamp":1781691572284,"version":"3.54.5"},"reference-count":109,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Innovation Capability Support Program of Shaanxi","award":["No. 2020KJXX-005"],"award-info":[{"award-number":["No. 2020KJXX-005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.<\/jats:p>","DOI":"10.3390\/rs12233854","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T08:59:15Z","timestamp":1606294755000},"page":"3854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments"],"prefix":"10.3390","volume":"12","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi\u2019an 710021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7396-4754","authenticated-orcid":false,"given":"Paraskevas","family":"Tsangaratos","sequence":"additional","affiliation":[{"name":"Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioanna","family":"Ilia","sequence":"additional","affiliation":[{"name":"Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1080\/13658816.2013.869821","article-title":"An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping","volume":"28","author":"Feizizadeh","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.geomorph.2015.03.016","article-title":"Spatial and temporal analysis of a global landslide catalog","volume":"249","author":"Kirschbaum","year":"2015","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1007\/s12517-012-0610-x","article-title":"Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms","volume":"6","author":"Zare","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"140549","DOI":"10.1016\/j.scitotenv.2020.140549","article-title":"Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping","volume":"742","author":"Hong","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/gto.12034","article-title":"Landslide prediction from machine learning","volume":"30","author":"Korup","year":"2014","journal-title":"Geol. Today"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s12665-018-7268-y","article-title":"Bagging based Support Vector Machines for spatial prediction of landslides","volume":"77","author":"Pham","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.envsoft.2009.10.016","article-title":"Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling","volume":"25","author":"Pradhan","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s12524-010-0020-z","article-title":"Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches","volume":"38","author":"Pradhan","year":"2010","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_11","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_12","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_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enggeo.2008.03.022","article-title":"Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"102","author":"Fell","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_16","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"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","article-title":"Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance","volume":"207","author":"Merghadi","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.catena.2017.05.034","article-title":"Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling","volume":"157","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.catena.2019.104425","article-title":"A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China","volume":"188","author":"Wang","year":"2020","journal-title":"Catena"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lai, J.-S., and Tsai, F. (2019). Improving GIS-based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning. Sensors, 19.","DOI":"10.3390\/s19173717"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"523","DOI":"10.3390\/ijgi3020523","article-title":"GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method","volume":"3","author":"Chalkias","year":"2014","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11069-018-3449-y","article-title":"A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping","volume":"94","author":"Ghorbanzadeh","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s12665-018-7548-6","article-title":"Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada, Greece","volume":"77","author":"Tsangaratos","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Roodposhti, M.S., Aryal, J., Shahabi, H., and Safarrad, T. (2016). Fuzzy Shannon Entropy: A Hybrid GIS-Based Landslide Susceptibility Mapping Method. Entropy, 18.","DOI":"10.3390\/e18100343"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Moharrami, M., Naboureh, A., Nachappa, T.G., Ghorbanzadeh, O., Guan, X., and Blaschke, T. (2020). National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060393"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mehrabi, M., Pradhan, B., Moayedi, H., and Alamri, A. (2020). Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques. Sensors, 20.","DOI":"10.3390\/s20061723"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.compgeo.2010.11.002","article-title":"Reliability analysis using radial basis function networks and support vector machines","volume":"38","author":"Tan","year":"2011","journal-title":"Comput. Geotech."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, X., Wang, Y., Niu, R., and Hu, Y. (2016). A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China. Int. J. Environ. Res. Public Health, 13.","DOI":"10.3390\/ijerph13050487"},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s10346-015-0565-6","article-title":"Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece","volume":"13","author":"Tsangaratos","year":"2016","journal-title":"Landslides"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s12665-016-6374-y","article-title":"Shallow landslide susceptibility assessment using a novel hybrid intelligence approach","volume":"76","author":"Shirzadi","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nguyen, V.V., Pham, B.T., Vu, B.T., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D.N., Kumar, R., and Chatterjee, J.M. (2019). Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling. Forests, 10.","DOI":"10.3390\/f10020157"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.enggeo.2008.01.004","article-title":"An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps","volume":"97","author":"Nefeslioglu","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2019). 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_40","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_41","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s12665-009-0394-9","article-title":"Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine","volume":"61","author":"Yilmaz","year":"2009","journal-title":"Environ. Earth Sci."},{"key":"ref_42","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_43","first-page":"139","article-title":"A Comparative Study of the Binary Logistic Regression (BLR) and Artificial Neural Network (ANN) Models for GIS-Based Spatial Predicting Landslides at a Regional Scale\u2014TXT-tool 1.081-6.1","volume":"Volume 1","author":"Shahabi","year":"2018","journal-title":"Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mutlu, B., Nefeslioglu, H.A., Sezer, E.A., Akcayol, M.A., and Gokceoglu, C. (2019). An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120578"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., 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_46","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_47","first-page":"629","article-title":"Breaking the curse of dimensionality with convex neural networks","volume":"18","author":"Bach","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1080\/17538947.2016.1169561","article-title":"Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: A case study in Central Vietnam","volume":"9","author":"Bui","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1016\/j.eswa.2010.08.123","article-title":"Housing price forecasting based on genetic algorithm and support vector machine","volume":"38","author":"Gu","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11069-013-0932-3","article-title":"Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models","volume":"71","author":"Nourani","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1080\/19475705.2019.1607782","article-title":"Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping","volume":"10","author":"Nguyen","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.catena.2018.12.033","article-title":"Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility","volume":"175","author":"Jaafari","year":"2019","journal-title":"Catena"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s12517-017-3002-4","article-title":"A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping","volume":"10","author":"Li","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1007\/s11069-020-04067-9","article-title":"Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping","volume":"103","author":"Paryani","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.enggeo.2015.04.004","article-title":"Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm","volume":"192","author":"Kavzoglu","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jhydrol.2016.06.027","article-title":"Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS","volume":"540","author":"Bui","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tsangaratos, P., and Ilia, I. (2017). Applying Machine Learning Algorithms in Landslide Susceptibility Assessments. Handbook of Neural Computation, Elsevier.","DOI":"10.1016\/B978-0-12-811318-9.00024-7"},{"key":"ref_58","unstructured":"Jones, P., and Harris, I. (2008). Climatic Research Unit (CRU) Time-Series Datasets of Variations in Climate with Variations in Other Phenomena, NCAS British Atmospheric Data Centre."},{"key":"ref_59","unstructured":"Rozos, D. (1989). Engineering-Geological Conditions in the Achaia County. Geomechanical Characteristics of the Plio-Pleistocene Sediments. [Ph.D. Thesis, University of Patras]."},{"key":"ref_60","first-page":"1","article-title":"Contribution \u00e0 L\u2019\u00e9tude G\u00e9ologique du Pinde Septentrional et D\u2019une Partie de la Mac\u00e9doine Occidentale","volume":"7","author":"Brunn","year":"1956","journal-title":"Ann. G\u00e9olog. Pays Hell\u00e9niques"},{"key":"ref_61","first-page":"1","article-title":"Contribution \u00e0 L\u2019\u00e9tude G\u00e9ologique de la Gr\u00e8ce Septentrionale: Les Confins de L\u2019epire et de la Thessalie","volume":"10","author":"Aubouin","year":"1959","journal-title":"Ann. G\u00e9olog. Pays Hell\u00e9niques"},{"key":"ref_62","unstructured":"Khun, M., Wing, J., and Weston, S. (2020, October 14). Caret: Classification and Regression Training. R Package Version 6.0-77. Available online: https:\/\/cran.microsoft.com\/snapshot\/2017-09-17\/web\/packages\/caret\/index.html."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1559\/152304087783875930","article-title":"Environmental Systems Research Institute Mapping","volume":"14","author":"Ryden","year":"1987","journal-title":"Am. Cartogr."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-020-8879-7","article-title":"Slow-moving landslides: Kinematic analysis and movement evolution modeling","volume":"79","author":"Kavoura","year":"2020","journal-title":"Environ. Earth Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/BF02910278","article-title":"The effect of spatial resolution on the accuracy of landslide susceptibility mapping: A case study in Boun, Korea","volume":"8","author":"Lee","year":"2004","journal-title":"Geosci. J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geomorph.2009.10.002","article-title":"Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA","volume":"115","author":"Regmi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.jseaes.2012.12.014","article-title":"A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey","volume":"64","author":"Ozdemir","year":"2013","journal-title":"J. Asian Earth Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. Stat. Appl. Earth Sci., 171\u2013183.","DOI":"10.4095\/128059"},{"key":"ref_69","first-page":"13","article-title":"Weights of Evidence Modeling And Weighted Logistic Regression For Mineral Potential Mapping","volume":"25","author":"Agterberg","year":"1993","journal-title":"Comput. Geol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s00477-020-01824-x","article-title":"Suitability of data preprocessing methods for landslide displacement forecasting","volume":"34","author":"Zou","year":"2020","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.5194\/nhess-12-1937-2012","article-title":"Logistic regression applied to natural hazards: Rare event logistic regression with replications","volume":"12","author":"Guns","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., Avand, M., Janizadeh, S., Kariminejad, N., Shahabi, H., Costache, R.-D., 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_73","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1984). Genetic Algorithms and Adaptation. Adaptive Control of Ill-Defined Systems, Springer.","DOI":"10.1007\/978-1-4684-8941-5_21"},{"key":"ref_74","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1214\/088342306000000493","article-title":"Support Vector Machines with Applications","volume":"21","author":"Moguerza","year":"2006","journal-title":"Stat. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Cherkassky, V., and Mulier, F.M. (2007). Learning from Data: Concepts, Theory, and Methods, John Wiley & Sons.","DOI":"10.1002\/9780470140529"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Tan, P.L., Tan, S.C., Lim, C.P., and Khor, S.E. (2011, January 14\u201317). A Modified Two-Stage Svm-Rfe Model for Cancer Classification Using Microarray Data. Proceedings of the International Conference on Neural Information Processing, Shanghai, China.","DOI":"10.1007\/978-3-642-24955-6_79"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer.","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_79","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_80","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_81","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual Comparisons by Ranking Methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"key":"ref_82","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_83","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_84","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_85","unstructured":"Varnes, D. (1984). IAEG Commission on Landslides and Other Mass Movements, Landslide Hazard Zonation: A Review of Principles and Practice, The UNESCO Press."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11069-018-3299-7","article-title":"Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models","volume":"93","author":"Polykretis","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_87","unstructured":"Koukis, G., Rozos, D., and Hadzinakos, I. (1997). Relationship Between Rainfall and Landslides in the Formations of Achaia County, Greece. Engineering Geology and the Environment, CRC Press."},{"key":"ref_88","unstructured":"Koukouvelas, I., and Doutsos, T. (1997). The Effects of Active Faults on the Generation of Landslides in Nw Peloponnese, Greece. Engineering Geology and the Environment, CRC Press."},{"key":"ref_89","unstructured":"Tsagas, D. (2011). Geomorphological Investigation and Mass Movements in Northern Peloponnese: Area of Xylokastro-Diakofto. [Ph.D. Thesis, University of Athens]."},{"key":"ref_90","unstructured":"NASA, Japan Space Systems, and US\/Japan Aster Science Team (2009). ASTER Global Digital Elevation Model V003, Data Set."},{"key":"ref_91","unstructured":"IGME (1980). Geological Map of Greece, at a Scale of 1:50,000, IGME. Available online: https:\/\/shop.geospatial.com\/product\/03-GRAC-Greece-50000-Geological-Maps."},{"key":"ref_92","unstructured":"IGME (2005). Geological Map of Greece, at a Scale of 1:50,000, IGME. Available online: https:\/\/shop.geospatial.com\/product\/03-GRAC-Greece-50000-Geological-Maps."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"193","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_94","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_95","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide risk assessment and management: An overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.catena.2015.07.020","article-title":"A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran","volume":"135","author":"Dehnavi","year":"2015","journal-title":"Catena"},{"key":"ref_97","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_98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2010\/901095","article-title":"Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey","volume":"2010","author":"Nefeslioglu","year":"2010","journal-title":"Math. Probl. Eng."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.geomorph.2014.09.020","article-title":"Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy)","volume":"242","author":"Conoscenti","year":"2015","journal-title":"Geomorphology"},{"key":"ref_100","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_101","first-page":"1","article-title":"Digital Terrain Analysis","volume":"Volume 479","author":"Wilson","year":"2000","journal-title":"Terrain Analysis: Principles and Applications"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Wang, G., Chen, X., and Chen, W. (2020). Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9030144"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10064-005-0023-0","article-title":"Landslide hazard and risk zonation\u2014Why is it still so difficult?","volume":"65","author":"Soeters","year":"2006","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"138595","DOI":"10.1016\/j.scitotenv.2020.138595","article-title":"A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility","volume":"726","author":"Arabameri","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_105","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_106","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s12665-016-6211-3","article-title":"Evaluation of different models in rainfall-triggered landslide susceptibility mapping: A case study in Chunan, southeast China","volume":"75","author":"Feng","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Luo, X., Lin, F., Zhu, S., Yu, M., Zhang, Z., Meng, L., and Peng, J. (2019). Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215134"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2004.10.004","article-title":"Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela","volume":"78","author":"Kavzoglu","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.5194\/nhess-10-1307-2010","article-title":"Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model","volume":"10","author":"Malpica","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:37:00Z","timestamp":1760179020000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,25]]},"references-count":109,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233854"],"URL":"https:\/\/doi.org\/10.3390\/rs12233854","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,25]]}}}