Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)
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Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75:1–20
Althuwaynee OF, Pradhan B, Lee S (2016) A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens 37:1190–1209
Andrews DW (1988) Chi-square diagnostic tests for econometric models: introduction and applications. J Econ 37:135–156
Booth AM et al (2015) Integrating diverse geologic and geodetic observations to determine failure mechanisms and deformation rates across a large bedrock landslide complex: the Osmundneset landslide, Sogn og Fjordane, Norway. Landslides 12:745–756
Carlini M et al (2016) Tectonic control on the development and distribution of large landslides in the northern Apennines (Italy). Geomorphology 253:425–437
Cawley GC, Talbot NL (2008) Efficient approximate leave-one-out cross-validation for kernel logistic regression. Mach Learn 71:243–264
Chen W, Panahi M, Pourghasemi HR (2017a) 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. Catena 157:310–324
Chen W et al (2017b) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Haz Risk 8:950–973
Chen W et al (2017c) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151:147–160
Chung C-JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472
Colkesen I, Sahin EK, Kavzoglu T (2016) Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci 118:53–64
Conoscenti C et al (2015) 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). Geomorphology 242:49–64
Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406
Cook TL, Yellen BC, Woodruff JD, Miller D (2015) Contrasting human versus climatic impacts on erosion. Geophys Res Lett 42:6680–6687
Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWAM) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 135:122–148
Dormann CF et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46
Dou J et al. (2014) GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan. In: Sassa K, Canuti P, Yin Y (eds) Landslide Science for a Safer Geoenvironment. Springer, Cham, pp 419–424
Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189
Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58
Gil D, Johnsson M (2010) Supervised SOM based architecture versus multilayer perceptron and RBF networks, Proceedings of the Linköping Electronic Conference, pp 15–24
Gorsevski PV, Brown MK, Panter K, Onasch CM (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides 13:467–484
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184
Hong H, Pradhan B, Xu C, Tien Bui D (2015a) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281
Hong H, Xu C, Revhaug I, Tien Bui D (2015b) Spatial prediction of landslide hazard at the Yihuang area (China): a comparative study on the predictive ability of backpropagation multi-layer perceptron neural networks and radial basic function neural networks. In: Robbi Sluter C, Madureira Cruz CB, Leal de Menezes PM (eds) Cartography – Maps Connecting the World. Springer, Cham, pp 175–188
Jaafari A, Najafi A, Pourghasemi H, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926
Kim T, Chung BD, Lee JS (2016) Incorporating receiver operating characteristics into naive Bayes for unbalanced data classification. Computing 99:1–16
Kimeldorf G, Wahba G (1971) Some results on Tchebycheffian spline functions. J Math Anal Appl 33:82–95
Kumar R, Anbalagan R (2015) Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS. J Earth Syst Sci 124:431–448
Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87:271–286
Lee C, Lee GG (2006) Information gain and divergence-based feature selection for machine learning-based text categorization. Inf Process Manag 42:155–165
Lineback Gritzner M, Marcus WA, Aspinall R, Custer SG (2001) Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology 37:149–165
Mercer J (1909) Functions of positive and negative type, and their connection with the theory of integral equations. Philos Trans R Soc Lond A 209:415–446
Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464
Peng JB et al (2015) Heavy rainfall triggered loess-mudstone landslide and subsequent debris flow in Tianshui, China. Eng Geol 186:79–90
Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia M (2017) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128:255–273
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 1–19
Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38:301–320
Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365
Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042
Rao J, Scott A (1987) On simple adjustments to chi-square tests with sample survey data. Ann Stat 385–397
Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Inf 8:867–883
Razavizadeh S, Solaimani K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76:499
Regmi AD et al (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742
Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology 115:172–187
Sar N, Khan A, Chatterjee S, Das A, Mipun BS (2016) Coupling of analytical hierarchy process and frequency ratio based spatial prediction of soil erosion susceptibility in Keleghai river basin, India. International Soil and Water Conservation Research
Satorra A, Bentler PM (2001) A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 66:507–514
Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
Shahabi H, Hashim M, Ahmad BB (2015) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environ Earth Sci 73:8647–8668
Soria D, Garibaldi JM, Ambrogi F, Biganzoli EM, Ellis IO (2011) A ‘non-parametric’version of the naive Bayes classifier. Knowl-Based Syst 24:775–784
Tien Bui D, Nguyen QP, Hoang N-D, Klempe H (2017) A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides 14:1–17
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Math Probl Eng 2012
Tien Bui D, Pradhan B, Revhaug I, Tran CT (2014) A comparative assessment between the application of fuzzy unordered rules induction algorithm and J48 decision tree models in spatial prediction of shallow landslides at Lang Son City, Vietnam. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote Sensing Applications in Environmental Research. Springer, New York, pp 87–111
Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) 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. Landslides 13:361–378
Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 145:164–179
Van Westen C (2004) Geo-information tools for landslide risk assessment: an overview of recent developments, Proceedings 9th International Symposium on Landslides. Balkema, Amsterdam, pp 39–56
Walter S (2002) Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med 21:1237–1256
Wang L-J, Guo M, Sawada K, Lin J, Zhang J (2016) A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J 20:117–136
Witten IH, Frank E, Mark AH (2011) Data mining: practical machine learning tools and techniques. 3rd edn. Morgan Kaufmann, Burlington
Wu YM, Lan HX, Gao X, Li LP, Yang ZH (2015) A simplified physically based coupled rainfall threshold model for triggering landslides. Eng Geol 195:63–69
Youssef AM, Pourghasemi HR, El-Haddad BA, Dhahry BK (2016) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir region, Saudi Arabia. Bull Eng Geol Environ 75:63–87
Zhang G et al (2016) Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena 142:233–244