Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

[1]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[2]  Nils J. Nilsson,et al.  Learning Machines: Foundations of Trainable Pattern-Classifying Systems , 1965 .

[3]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[4]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[5]  P. Royston,et al.  Generalized additive models , 1998 .

[6]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  C. F. Lee,et al.  Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .

[9]  Anthony Lehmann,et al.  GRASP: generalized regression analysis and spatial prediction , 2002 .

[10]  F. Dominici,et al.  On the use of generalized additive models in time-series studies of air pollution and health. , 2002, American journal of epidemiology.

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

[12]  T. Topal,et al.  GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey) , 2003 .

[13]  Andrea G. Fabbri,et al.  Validation of Spatial Prediction Models for Landslide Hazard Mapping , 2003 .

[14]  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 .

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

[16]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[17]  Barnali M. Dixon,et al.  Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis , 2005 .

[18]  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 .

[19]  H. A. Nefeslioglu,et al.  Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey , 2006 .

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

[21]  Ahmed Tahour,et al.  Adaptive neuro-fuzzy controller of switched reluctance motor , 2007 .

[22]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[23]  S. Gruber,et al.  Permafrost in steep bedrock slopes and its temperature‐related destabilization following climate change , 2007 .

[24]  N. W. Park,et al.  Quantitative assessment of landslide susceptibility using high‐resolution remote sensing data and a generalized additive model , 2008 .

[25]  Elif Derya íbeyli.  Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders , 2008 .

[26]  P. Magliulo,et al.  Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy , 2008 .

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

[28]  Greg P. Kotze,et al.  Landslide susceptibility and hazard mapping in Australia for land-use planning — with reference to challenges in metropolitan suburbia , 2008 .

[29]  H. A. Nefeslioglu,et al.  Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) , 2008 .

[30]  Chia-Nan Liu,et al.  Integrating GIS and stress transfer mechanism in mapping rainfall-triggered landslide susceptibility , 2008 .

[31]  Javier Roca-Pardiñas,et al.  Categorical variables, interactions and generalized additive models. Applications in computer-aided diagnosis systems , 2008, Comput. Biol. Medicine.

[32]  Isik Yilmaz,et al.  Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) , 2009, Comput. Geosci..

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

[34]  Thong Ngee Goh,et al.  Adaptive ridge regression system for software cost estimating on multi-collinear datasets , 2010, J. Syst. Softw..

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

[36]  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..

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

[38]  S. Reis,et al.  A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics , 2011 .

[39]  A. Brenning,et al.  Integrating physical and empirical landslide susceptibility models using generalized additive models , 2011 .

[40]  Chiara Lepore,et al.  Rainfall-induced landslide susceptibility zonation of Puerto Rico , 2012, Environmental Earth Sciences.

[41]  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..

[42]  D. Bui,et al.  Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression , 2011 .

[43]  B. Pradhan,et al.  Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models , 2012 .

[44]  B. Pradhan,et al.  Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya , 2012, Natural Hazards.

[45]  Candan Gokceoglu,et al.  Discussion on “Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS” by Choi et al. (2012), Engineering Geology, 124, 12–23 , 2012 .

[46]  Effect of lithological data of different scales on modelling landslide susceptibility maps , 2012 .

[47]  Hongey Chen,et al.  Various links between landslide debris and sediment flux during earthquake and rainstorm events , 2012 .

[48]  B. Pradhan,et al.  Remote Sensing Data Derived Parameters and its Use in Landslide Susceptibility Assessment Using Shannon’s Entropy and GIS , 2012 .

[49]  Chong Xu,et al.  GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China , 2012 .

[50]  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.

[51]  Bakhtiar Feizizadeh,et al.  Integrating GIS Based Fuzzy Set Theory in Multicriteria Evaluation Methods for Landslide Susceptibility Mapping , 2013 .

[52]  Honglin He,et al.  Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China , 2013, Natural Hazards.

[53]  C. Gokceoglu,et al.  GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran) , 2014, Arabian Journal of Geosciences.

[54]  Chung-Pai Chang,et al.  Density Distribution of Landslides Triggered by the 2008 Wenchuan Earthquake and their Relationships to Peak Ground Acceleration , 2013 .

[55]  T. Blaschke,et al.  Landslide Susceptibility Mapping for the Urmia Lake basin, Iran: A multi- Criteria Evaluation Approach using GIS , 2013 .

[56]  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..

[57]  B. Pradhan,et al.  Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran , 2013, Journal of Earth System Science.

[58]  Sonochemical degradation of endocrine disrupting chemicals 17β-estradiol and 17α-ethinylestradiol in water and wastewater , 2014, International Journal of Environmental Science and Technology.

[59]  Aini Hussain,et al.  Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS) , 2013, Water Resources Management.

[60]  Xing-guo Yang,et al.  Dynamic process analysis for the initiation and movement of the Donghekou landslide-debris flow triggered by the Wenchuan earthquake , 2013 .

[61]  Jin-King Liu,et al.  Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images , 2013, Remote. Sens..

[62]  Thomas Blaschke,et al.  A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆ , 2014, Comput. Geosci..

[63]  A. Brenning,et al.  Assessing the quality of landslide susceptibility maps – case study Lower Austria , 2014 .

[64]  B. Pradhan,et al.  Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models , 2015, Geosciences Journal.

[65]  P. Reichenbach,et al.  The Influence of Land Use Change on Landslide Susceptibility Zonation: The Briga Catchment Test Site (Messina, Italy) , 2013, Environmental Management.

[66]  Wenjun Zheng,et al.  Landslides triggered by the 22 July 2013 Minxian–Zhangxian, China, Mw 5.9 earthquake: Inventory compiling and spatial distribution analysis , 2014 .

[67]  Thomas Blaschke,et al.  An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping , 2014, Int. J. Geogr. Inf. Sci..

[68]  Umi Kalthum Ngah,et al.  Intelligent Landslide System Based on Discriminant Analysis and Cascade-Forward Back-Propagation Network , 2014, Arabian Journal for Science and Engineering.

[69]  H. Pourghasemi,et al.  GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran , 2014, International Journal of Environmental Science and Technology.

[70]  Chao-Lung Tang,et al.  Effect of the acceleration component normal to the sliding surface on earthquake-induced landslide triggering , 2015, Landslides.

[71]  Hamid Reza Pourghasemi,et al.  Landslide susceptibility mapping along Bhalubang — Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models , 2014, Journal of Mountain Science.

[72]  Biswajeet Pradhan,et al.  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 , 2016, Landslides.

[73]  E. Rotigliano,et al.  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) , 2015 .

[74]  B. Pham,et al.  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 , 2017, Theoretical and Applied Climatology.

[75]  Wei Chen,et al.  Application of frequency ratio and weights of evidence models in landslide susceptibility mapping for the Shangzhou District of Shangluo City, China , 2015, Environmental Earth Sciences.

[76]  Cheng Su,et al.  Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine , 2015, Natural Hazards.

[77]  Mustafa Neamah Jebur,et al.  Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines , 2015, Environmental Earth Sciences.

[78]  Changjiang Li,et al.  Rainfall intensity–duration thresholds for the initiation of landslides in Zhejiang Province, China , 2015 .

[79]  H. Pourghasemi,et al.  Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia , 2016, Bulletin of Engineering Geology and the Environment.

[80]  Moung-Jin Lee,et al.  Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea , 2015, Environmental Earth Sciences.

[81]  B. Pradhan,et al.  Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines , 2015 .

[82]  Hamid Reza Pourghasemi,et al.  Erratum to: 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 , 2016, Landslides.

[83]  V. Moosavi,et al.  Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping , 2016, Landslides.

[84]  Iman Nasiri Aghdam,et al.  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 , 2015 .

[85]  F. Gutiérrez,et al.  Large landslides associated with a diapiric fold in Canelles Reservoir (Spanish Pyrenees): Detailed geological–geomorphological mapping, trenching and electrical resistivity imaging , 2015 .

[86]  Liangjie Wang,et al.  Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models , 2015 .

[87]  H. Pourghasemi,et al.  Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran , 2016, Environmental Earth Sciences.

[88]  Wei Chen,et al.  A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping , 2016, Arabian Journal of Geosciences.

[89]  Iman Nasiri Aghdam,et al.  Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran) , 2016, Environmental Earth Sciences.

[90]  Zhujun Han,et al.  Newmark displacement model for landslides induced by the 2013 Ms 7.0 Lushan earthquake, China , 2016, Frontiers of Earth Science.

[91]  Yangshuang Wang,et al.  Modeling of landslide generated impulsive waves considering complex topography in reservoir area , 2016, Environmental Earth Sciences.

[92]  Huichan Chai,et al.  Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China , 2016, Environmental Earth Sciences.

[93]  Paraskevas Tsangaratos,et al.  Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size , 2016 .

[94]  Biswajeet Pradhan,et al.  A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) , 2016, Environ. Model. Softw..

[95]  M. Rossi,et al.  Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods , 2017, Theoretical and Applied Climatology.

[96]  Nguyen Quoc Thanh,et al.  Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization , 2017, Landslides.

[97]  Jun Chen,et al.  Geochemical characteristics of the giant Nibao Carlin-type gold deposit (Guizhou, China) and their geological implications , 2016, Arabian Journal of Geosciences.

[98]  Wei Chen,et al.  Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping , 2016 .

[99]  B. Pham,et al.  A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area , 2016, Geotechnical and Geological Engineering.

[100]  Zohre Sadat Pourtaghi,et al.  Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .

[101]  Ryuichi Yatabe,et al.  Logistic regression and artificial neural network models for mapping of regional-scale landslide susceptibility in volcanic mountains of West Java (Indonesia) , 2016 .

[102]  Dieu Tien Bui,et al.  Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions , 2016, Environmental Earth Sciences.

[103]  Zohre Sadat Pourtaghi,et al.  Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models , 2016 .

[104]  M. Zare,et al.  Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods , 2016 .

[105]  M. Sciarra,et al.  Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy) , 2017, Bulletin of Engineering Geology and the Environment.

[106]  A. Erener,et al.  A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM) , 2016 .

[107]  Biswajeet Pradhan,et al.  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 , 2016 .

[108]  T. Kavzoglu,et al.  Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression , 2016 .

[109]  Yujun Yi,et al.  A habitat suitability model for Chinese sturgeon determined using the generalized additive method , 2016 .

[110]  A. Kornejady,et al.  Landslide susceptibility assessment using maximum entropy model with two different data sampling methods , 2017 .

[111]  B. Pradhan,et al.  A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .

[112]  Seyed Amir Naghibi,et al.  A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China , 2018, Bulletin of Engineering Geology and the Environment.

[113]  Wei Chen,et al.  A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping , 2017 .

[114]  Wei Chen,et al.  Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques , 2017 .

[115]  Dieu Tien Bui,et al.  Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS , 2017 .

[116]  H. Pourghasemi,et al.  Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. , 2017, The Science of the total environment.

[117]  Seyed Amir Naghibi,et al.  Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms , 2018, Bulletin of Engineering Geology and the Environment.

[118]  Hamid Reza Pourghasemi,et al.  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 , 2017 .

[119]  Wei Chen,et al.  GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models , 2017 .

[120]  A. Kornejady,et al.  Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND , 2018 .