A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India
暂无分享,去创建一个
D. Bui | B. Pham | M. Dholakia | Indra Prakash | A. Shirzadi
[1] E. B. Wilson,et al. The Distribution of Chi-Square. , 1931, Proceedings of the National Academy of Sciences of the United States of America.
[2] C. J. Burke,et al. The use and misuse of the chi-square test. , 1949, Psychological bulletin.
[3] D. Varnes. SLOPE MOVEMENT TYPES AND PROCESSES , 1978 .
[4] D. Varnes. Landslide hazard zonation: A review of principles and practice , 1984 .
[5] J A Swets,et al. Measuring the accuracy of diagnostic systems. , 1988, Science.
[6] Theo van Asch,et al. Deterministic Modelling in Gis-Based Landslide Hazard Assessment , 1995 .
[7] S. Kramer. Geotechnical Earthquake Engineering , 1996 .
[8] C. F. Lee,et al. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .
[9] Fuchu Dai,et al. Landslide risk assessment and management: an overview , 2002 .
[10] John C. Davis,et al. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA , 2003 .
[11] Saro Lee,et al. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .
[12] D. P. Kanungo,et al. An Integrated Approach for Landslide Susceptibility Mapping Using Remote Sensing and GIS , 2004 .
[13] T. Kavzoglu,et al. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela , 2005 .
[14] Candan Gokceoglu,et al. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity , 2005 .
[15] L. Ayalew,et al. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .
[16] Xu Weiya,et al. GIS-based landslide hazard assessment: an overview , 2005 .
[17] 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 .
[18] L. Ermini,et al. Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .
[19] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] M. Eeckhaut,et al. Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium) , 2006 .
[21] B. Pradhan,et al. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .
[22] Zhi-Hua Zhou,et al. Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[23] 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 .
[24] Chun-Xia Zhang,et al. RotBoost: A technique for combining Rotation Forest and AdaBoost , 2008, Pattern Recognit. Lett..
[25] M. Matteucci,et al. Artificial neural networks and cluster analysis in landslide susceptibility zonation , 2008 .
[26] De-Shuang Huang,et al. Cancer classification using Rotation Forest , 2008, Comput. Biol. Medicine.
[27] I. Yilmaz. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .
[28] H. A. Nefeslioglu,et al. Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey , 2010 .
[29] B. Pradhan. Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling , 2011, Environmental and Ecological Statistics.
[30] Young-Kwang Yeon,et al. Landslide susceptibility mapping in Injae, Korea, using a decision tree , 2010 .
[31] Mukta Sharma,et al. Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[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] Arif Gülten,et al. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..
[35] A. Ozdemir. Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey) , 2011 .
[36] S. Reis,et al. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics , 2011 .
[37] M. Marjanović,et al. Landslide susceptibility assessment using SVM machine learning algorithm , 2011 .
[38] Hao Yu,et al. Advantages of Radial Basis Function Networks for Dynamic System Design , 2011, IEEE Transactions on Industrial Electronics.
[39] 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..
[40] B. Pradhan,et al. Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models , 2012 .
[41] B. Pradhan,et al. 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 , 2013, Arabian Journal of Geosciences.
[42] R. Bai,et al. Fractal dimension analysis of higher-order mode shapes for damage identification of beam structures , 2012 .
[43] Dieu Tien Bui,et al. Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis , 2012 .
[44] Fausto Guzzetti,et al. Tier-based approaches for landslide susceptibility assessment in Europe , 2013, Landslides.
[45] Abbas Bahroudi,et al. Support vector machine for multi-classification of mineral prospectivity areas , 2012, Comput. Geosci..
[46] B. Pradhan,et al. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.
[47] B. Pradhan,et al. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran , 2013, Journal of Earth System Science.
[48] Paraskevas Tsangaratos,et al. Estimating landslide susceptibility through a artificial neural network classifier , 2014, Natural Hazards.
[49] E. Rotigliano,et al. Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy) , 2014 .
[50] Mustafa Neamah Jebur,et al. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia , 2014 .
[51] T. Kavzoglu,et al. An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district , 2015, Natural Hazards.
[52] H. S. Lim,et al. Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model , 2015, Arabian Journal of Geosciences.
[53] S. Río,et al. Links between teleconnection patterns and mean temperature in Spain , 2015, Theoretical and Applied Climatology.
[54] Manuel Graña,et al. Hybrid extreme rotation forest , 2014, Neural Networks.
[55] 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 .
[56] Umi Kalthum Ngah,et al. Modeling and Testing Landslide Hazard Using Decision Tree , 2014, J. Appl. Math..
[57] Biswajeet Pradhan,et al. A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping , 2014, Landslides.
[58] M.N.S. Swamy,et al. Radial Basis Function Networks , 2014 .
[59] 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.
[60] I. Ilia,et al. Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map , 2016, Landslides.
[61] I. Ilia,et al. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece , 2016, Landslides.
[62] Fengyin Liu,et al. Study on the Reasonable Height of Embankment in Qinghai–Tibet Highway , 2016, Geotechnical and Geological Engineering.
[63] 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.
[64] Heping Tao,et al. Dynamic monitoring of soil wind erosion in Inner Mongolia of China during 1985–2011 based on geographic information system and remote sensing , 2016, Natural Hazards.
[65] Zohre Sadat Pourtaghi,et al. 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 , 2015, Landslides.
[66] O. Igwe. The geotechnical characteristics of landslides on the sedimentary and metamorphic terrains of South-East Nigeria, West Africa , 2015, Geoenvironmental Disasters.
[67] Dieu Tien Bui,et al. Landslide Susceptibility Assessment at a Part of Uttarakhand Himalaya, India using GIS – based Statistical Approach of Frequency Ratio Method , 2015 .
[68] R. Anbalagan,et al. Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim , 2015, Geoenvironmental Disasters.
[69] Paraskevas Tsangaratos,et al. A Geographical Information System (GIS) Based Probabilistic Certainty Factor Approach in Assessing Landslide Susceptibility: The Case Study of Kimi, Euboea, Greece , 2015 .
[70] 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.
[71] Biswajeet Pradhan,et al. Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of Landslides Using Novel Ensemble Evidential Belief Functions and Support Vector Machine Models in GIS , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[72] Chong Xu,et al. 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 , 2015 .
[73] 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 .
[74] Binh Thai Pham,et al. Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS , 2016 .
[75] Guo Huadong,et al. Digital Earth and Future Earth , 2016 .
[76] U. Haque,et al. Fatal landslides in Europe , 2016, Landslides.
[77] Seung-Rae Lee,et al. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea , 2016 .
[78] 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 .
[79] 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..
[80] 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.
[81] 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.
[82] Nhat-Duc Hoang,et al. A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides , 2016, J. Comput. Civ. Eng..
[83] T. Glade,et al. Landslide-driven erosion and slope–channel coupling in steep, forested terrain, Ruahine Ranges, New Zealand, 1946–2011 , 2016 .
[84] George L. W. Perry,et al. Identifying the controls on coastal cliff landslides using machine-learning approaches , 2016, Environ. Model. Softw..
[85] S. L. Gariano,et al. Landslides in a changing climate , 2016 .
[86] Nhat-Duc Hoang,et al. 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 , 2016, Int. J. Digit. Earth.
[87] B. Pradhan,et al. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks , 2016, Environmental Earth Sciences.
[88] Wei Chen,et al. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China , 2017, Landslides.
[89] 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 .
[90] B. Pham,et al. Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS , 2016, Natural Hazards.
[91] Landslide risk management in Norway , 2017 .
[92] D. Bui,et al. A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS , 2017 .
[93] D. Bui,et al. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach , 2017, Environmental Earth Sciences.
[94] 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 .
[95] T. Oguchi,et al. Effect of forest clear-cutting on landslide occurrences: Analysis of rainfall thresholds at Mt. Ichifusa, Japan , 2017 .
[96] Paraskevas Tsangaratos,et al. APPLICATION OF A BAYESIAN APPROACH IN GIS BASED MODEL FOR EVALUATING LANDSLIDE SUSCEPTIBILITY. CASE STUDY KIMI AREA, EUBOEA, GREECE , 2017 .