Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models

[1]  D. Varnes.  SLOPE MOVEMENT TYPES AND PROCESSES , 1978 .

[2]  E. E. Brabb.  Innovative approaches to landslide hazard and risk mapping , 1985 .

[3]  C. Gokceoğlu,et al.  Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach , 2002 .

[4]  L. Ayalew,et al.  Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan , 2004 .

[5]  V. Doyuran,et al.  Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey , 2004 .

[6]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[7]  P. Reichenbach,et al.  Estimating the quality of landslide susceptibility models , 2006 .

[8]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[9]  Saro Lee.  Application and verification of fuzzy algebraic operators to landslide susceptibility mapping , 2007 .

[10]  B. Pradhan,et al.  Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .

[11]  Aykut Akgün,et al.  GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region , 2007 .

[12]  Saro Lee.  Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea , 2007 .

[13]  Maria Petrou,et al.  Landslide Possibility Mapping Using Fuzzy Approaches , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[14]  L. Tham,et al.  Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .

[15]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[16]  H. A. Nefeslioglu,et al.  Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey) , 2008 .

[17]  Manoj K. Arora,et al.  Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas , 2008 .

[18]  Hyun-Joo Oh,et al.  Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand , 2009 .

[19]  M. Matteucci,et al.  Artificial neural networks and cluster analysis in landslide susceptibility zonation , 2008 .

[20]  B. Pradhan,et al.  Landslide risk analysis using artificial neural network model focussing on different training sites. , 2009 .

[21]  H. Saito,et al.  Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan , 2009 .

[22]  Chein-Lee Wang,et al.  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 , 2009 .

[23]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[24]  Abbas Alimohammadi,et al.  A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping , 2010, Comput. Geosci..

[25]  Young-Kwang Yeon,et al.  Landslide susceptibility mapping in Injae, Korea, using a decision tree , 2010 .

[26]  B. Pradhan,et al.  Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia , 2010 .

[27]  Netra R. Regmi,et al.  Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA , 2010 .

[28]  S. Bai,et al.  GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China , 2010 .

[29]  A. Shakoor,et al.  A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses , 2010 .

[30]  B. Pradhan,et al.  Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models , 2010 .

[31]  A. Stein,et al.  Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India) , 2010 .

[32]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[33]  Chandra Prakash Poudyal,et al.  Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya , 2010 .

[34]  B. Pradhan.  Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches , 2010 .

[35]  Peter M. Atkinson,et al.  Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy , 2011 .

[36]  Biswajeet Pradhan,et al.  Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area , 2011, Comput. Geosci..

[37]  Xiwei Xu,et al.  Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China , 2012, Comput. Geosci..

[38]  B. Pradhan,et al.  Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks , 2012 .

[39]  Á. Felicísimo,et al.  Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study , 2013, Landslides.

[40]  A. Ozdemir,et al.  A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .

[41]  T. Kavzoglu,et al.  Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression , 2014, Landslides.

[42]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[43]  Kazuhide Sawada,et al.  Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy , 2013, Comput. Geosci..

[44]  Mustafa Aytekin,et al.  Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey) , 2015, Arabian Journal of Geosciences.

[45]  H. Shahabi,et al.  Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models , 2014 .

[46]  S. Pascale,et al.  Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy) , 2014 .

[47]  Jung Hyun Lee,et al.  A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping , 2014 .