{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T11:58:40Z","timestamp":1774439920718,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,11]],"date-time":"2017-09-11T00:00:00Z","timestamp":1505088000000},"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":["61271408"],"award-info":[{"award-number":["61271408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neighborhoods. Based on these facts, the main idea of this research is to group a study area into several clusters to ensure that landslides in each cluster are affected by the same set of selected causative factors. Based on this idea, the proposed predictive method is constructed for accurate LSM at a regional scale by applying a statistical model to each cluster of the study area. Specifically, each causative factor is first classified by the natural breaks method with the optimal number of classes, which is determined by adopting Shannon\u2019s entropy index. Then, a certainty factor (CF) for each class of factors is estimated. The selection of the causative factors for each cluster is determined based on the CF values of each factor. Furthermore, the logistic regression model is used as an example of statistical models in each cluster using the selected causative factors for landslide prediction. Finally, a global landslide susceptibility map is obtained by combining the regional maps. Experimental results based on both qualitative and quantitative analysis indicated that the proposed framework can achieve more accurate landslide susceptibility maps when compared to some existing methods, e.g., the proposed framework can achieve an overall prediction accuracy of 91.76%, which is 7.63\u201311.5% higher than those existing methods. Therefore, the local scale LSM technique is very promising for further improvement of landslide prediction.<\/jats:p>","DOI":"10.3390\/rs9090938","type":"journal-article","created":{"date-parts":[[2017,9,11]],"date-time":"2017-09-11T10:28:46Z","timestamp":1505125726000},"page":"938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China"],"prefix":"10.3390","volume":"9","author":[{"given":"Qian","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-7030","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ruiqing","family":"Niu","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ling","family":"Peng","sequence":"additional","affiliation":[{"name":"China Institute of Geo-Environment Monitoring, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, C., Liu, Y., Wen, M., Li, T., Lian, J., and Qin, S. (2009). Geo-hazard initiation and assessment in the three gorges reservoir. Landslide Disaster Mitigation in Three Gorges Reservoir, China, Springer.","DOI":"10.1007\/978-3-642-00132-1_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.jseaes.2009.01.002","article-title":"A comparative study of Dempster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: An experience from Zagros Mountains, SW Iran","volume":"35","author":"Tangestani","year":"2009","journal-title":"J. Asian Earth Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0169-555X(95)00071-C","article-title":"Remote sensing techniques for landslide studies and hazard zonation in Europe","volume":"15","author":"Mantovani","year":"1996","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rse.2005.08.004","article-title":"Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments","volume":"98","author":"Metternicht","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote sensing for landslide investigations: An overview of recent achievements and perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.geomorph.2010.05.009","article-title":"Assessing susceptibility to landslides: Using models to understand observed changes in slopes","volume":"122","author":"Regmi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.geomorph.2008.03.003","article-title":"GIS-based landslide susceptibility mapping for the 2005 kashmir earthquake region","volume":"101","author":"Kamp","year":"2008","journal-title":"Geomorphology"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.catena.2016.03.028","article-title":"Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China","volume":"142","author":"Zhang","year":"2016","journal-title":"CATENA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1007\/s00254-001-0454-2","article-title":"Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach","volume":"41","author":"Gokceoglu","year":"2002","journal-title":"Environ. Geol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1080\/136588100240903","article-title":"Application of fuzzy measures in multi-criteria evaluation in GIS","volume":"14","author":"Jiang","year":"2000","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.catena.2016.01.022","article-title":"Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping","volume":"140","author":"Pradhan","year":"2016","journal-title":"CATENA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s00254-007-0882-8","article-title":"Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models","volume":"54","author":"Akgun","year":"2008","journal-title":"Environ. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.enggeo.2005.08.004","article-title":"Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications","volume":"81","author":"Ayalew","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1080\/17538947.2012.749950","article-title":"GIS-based ordered weighted averaging and Dempster-Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran","volume":"7","author":"Feizizadeh","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.ecolind.2013.06.005","article-title":"Ecological land suitability analysis through spatial indicators: An application of the analytic network process technique and ordered weighted average approach","volume":"34","author":"Ferretti","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/BF02272809","article-title":"The representation of geoscience information for data integration","volume":"2","author":"Chung","year":"1993","journal-title":"Nat. Resour. Res."},{"key":"ref_20","unstructured":"Chung, C., and Fabbri, A. (1998, January 3\u20137). Three bayesian prediction models for landslide hazard. Proceedings of the International Association for Mathematical Geology 1998 Annual Meeting, Ischia, Italy."},{"key":"ref_21","first-page":"1389","article-title":"Probabilistic prediction models for landslide hazard mapping","volume":"65","author":"Chung","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10346-004-0039-8","article-title":"An approach for GIS-based statistical landslide susceptibility zonation\u2014With a case study in the Himalayas","volume":"2","author":"Saha","year":"2005","journal-title":"Landslides"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s00267-012-9921-7","article-title":"Landslide susceptibility assessment and validation in the framework of municipal planning in Portugal: The case of Loures municipality","volume":"50","author":"Guillard","year":"2012","journal-title":"Environ. Manag."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/19475705.2010.498151","article-title":"Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area","volume":"1","author":"Pradhan","year":"2010","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1080\/13658810410001702003","article-title":"Landslide susceptibility mapping using GIS and the weight-of-evidence model","volume":"18","author":"Lee","year":"2004","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.geomorph.2010.02.017","article-title":"Analysis of landslide inventories for accurate prediction of debris-flow source areas","volume":"119","author":"Blahut","year":"2010","journal-title":"Geomorphology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geomorph.2017.04.002","article-title":"A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China","volume":"290","author":"Hong","year":"2017","journal-title":"Geomorphology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s12665-009-0245-8","article-title":"Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models","volume":"60","author":"Pradhan","year":"2009","journal-title":"Environ. Earth Sci."},{"key":"ref_31","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":"2006","journal-title":"Landslides"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s10661-011-1996-8","article-title":"Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: A case study at Penang Island, Malaysia","volume":"184","author":"Pradhan","year":"2012","journal-title":"Environ. Monit. Assess."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/19475705.2012.662915","article-title":"A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS","volume":"4","author":"Pourghasemi","year":"2013","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_34","first-page":"209","article-title":"Recommendations for the quantitative analysis of landslide risk","volume":"73","author":"Corominas","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s11069-006-9061-6","article-title":"Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada","volume":"42","author":"Chen","year":"2007","journal-title":"Nat. Hazards"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.geomorph.2005.05.011","article-title":"Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses","volume":"72","author":"Can","year":"2005","journal-title":"Geomorphology"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s11069-007-9190-6","article-title":"Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: The Melen Gorge (NW Turkey)","volume":"46","author":"Gorum","year":"2008","journal-title":"Nat. Hazards"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s00254-007-0788-5","article-title":"Extraction of potential debris source areas by logistic regression technique: A case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey)","volume":"54","author":"Tunusluoglu","year":"2007","journal-title":"Environ. Geol."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.enggeo.2011.09.011","article-title":"Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using aster images and GIS","volume":"124","author":"Choi","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.1007\/s10661-011-2352-8","article-title":"Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)","volume":"184","author":"Akgun","year":"2012","journal-title":"Environ. Monit. Assess."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.geomorph.2009.04.004","article-title":"Discriminant analysis of the geomorphic characteristics and stability of landslide dams","volume":"110","author":"Dong","year":"2009","journal-title":"Geomorphology"},{"key":"ref_45","unstructured":"Gartner, G., and Ortag, F. (2010). Detailed mapping of landslide susceptibility for urban planning purposes in carpathian and subcarpathian towns of Romania. Cartography in Central and Eastern Europe: CEE 2009, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/0143116050030042","article-title":"A land-cover classification for landslide susceptibility mapping by using feature components","volume":"27","author":"Yesilnacar","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.geomorph.2009.02.026","article-title":"Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi mountains, Japan","volume":"109","author":"Saito","year":"2009","journal-title":"Geomorphology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazards Earth Syst."},{"key":"ref_50","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_51","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_52","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s00254-008-1350-9","article-title":"A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the southern cross-island highway in Taiwan","volume":"57","author":"Chen","year":"2009","journal-title":"Environ. Geol."},{"key":"ref_53","first-page":"1","article-title":"Landslide risk analysis using artificial neural network model focussing on different training sites","volume":"4","author":"Pradhan","year":"2009","journal-title":"Int. J. Phys. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11806-010-0236-7","article-title":"Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model","volume":"13","author":"Pradhan","year":"2010","journal-title":"Geospat. Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.envsoft.2016.07.016","article-title":"Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping","volume":"84","author":"Arnone","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"CATENA"},{"key":"ref_57","first-page":"1","article-title":"Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and na\u00efve bayes models","volume":"2012","author":"Pradhan","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_58","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":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_59","first-page":"12","article-title":"Landslide susceptibility assessment in the Hoa Binh Province of Vietnam: A comparison of the levenberg-marquardt and bayesian regularized neural networks","volume":"171\u2013172","author":"Pradhan","year":"2012","journal-title":"Geomorphology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.compenvurbsys.2009.12.004","article-title":"A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses","volume":"34","author":"Pradhan","year":"2010","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s10346-011-0257-9","article-title":"Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India","volume":"8","author":"Das","year":"2011","journal-title":"Landslides"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10346-009-0188-x","article-title":"Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)","volume":"7","author":"Erener","year":"2010","journal-title":"Landslides"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/584091.584093","article-title":"A mathematical theory of communication","volume":"5","author":"Shannon","year":"2001","journal-title":"ACM SIGMOBILE Mobile Comput. Commun. Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s12665-010-0724-y","article-title":"Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania)","volume":"63","author":"Constantin","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.pce.2009.12.002","article-title":"Landslide susceptibility assessment of the Kra\u2019ovany-Liptovsk\u00fd Mikul\u00e1\u0161 railway case study","volume":"35","author":"Bednarik","year":"2010","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1007\/s11069-011-9847-z","article-title":"Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides","volume":"59","author":"Kanungo","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","article-title":"The global K-means clustering algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern Recogn."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ding, C., and He, X. (2004, January 4\u20138). K-means clustering via principal component analysis. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada.","DOI":"10.1145\/1015330.1015408"},{"key":"ref_74","unstructured":"Alsabti, K., Ranka, S., and Singh, V. (1997). An efficient K-means clustering algorithm. Electr. Eng. Comput. Sci., 43."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","year":"2007","journal-title":"Qual. Quant."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1080\/00401706.1970.10488699","article-title":"Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation","volume":"12","author":"Marquaridt","year":"1970","journal-title":"Technometrics"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.geomorph.2009.09.023","article-title":"Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)","volume":"114","author":"Das","year":"2010","journal-title":"Geomorphology"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges Area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2009.10.001","article-title":"A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses","volume":"110","author":"Nandi","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_80","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_81","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.geomorph.2006.02.011","article-title":"Spatially and temporally distributed modeling of landslide susceptibility","volume":"80","author":"Gorsevski","year":"2006","journal-title":"Geomorphology"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2011.04.012","article-title":"An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm","volume":"38","author":"Akgun","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s12524-012-0255-y","article-title":"Remote sensing and GIS based landslide susceptibility assessment using binary logistic regression model: A case study in the Ganeshganga watershed, Himalayas","volume":"41","author":"Kundu","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"395","DOI":"10.5194\/nhess-13-395-2013","article-title":"Landslide susceptibility assessment by using a neuro-fuzzy model: A case study in the Rupestrian heritage rich area of Matera","volume":"13","author":"Sdao","year":"2013","journal-title":"Nat. Hazards Earth Syst."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Tsangaratos, P., Ilia, I., Hong, H., Chen, W., and Xu, C. (2016). Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng county, China. Landslides, 1\u201321.","DOI":"10.1007\/s10346-016-0769-4"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s12665-011-1263-x","article-title":"Potential suitability for urban planning and industry development using natural hazard maps and geological-geomorphological parameters","volume":"66","author":"Bathrellos","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.scitotenv.2016.10.025","article-title":"Suitability estimation for urban development using multi-hazard assessment map","volume":"575","author":"Bathrellos","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0013-7952(01)00028-X","article-title":"GIS-based geo-environmental evaluation for urban land-use planning: A case study","volume":"61","author":"Dai","year":"2001","journal-title":"Eng. Geol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.landurbplan.2004.05.001","article-title":"Urban land-use allocation in a mediterranean ecotone: Habitat heterogeneity model incorporated in a GIS using a multi-criteria mechanism","volume":"72","author":"Svoray","year":"2005","journal-title":"Landsc. Urban Plan."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1016\/j.jaridenv.2008.06.005","article-title":"Regional assessment of environmental vulnerability in the Tibetan Plateau: Development and application of a new method","volume":"72","author":"Wang","year":"2008","journal-title":"J. Arid Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:39Z","timestamp":1760208279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,11]]},"references-count":91,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090938"],"URL":"https:\/\/doi.org\/10.3390\/rs9090938","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,11]]}}}