An objective absence data sampling method for landslide susceptibility mapping

[1]  Yasin Wahid Rabby,et al.  Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods , 2020, Geocarto International.

[2]  Rashedur M. Rahman,et al.  Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping , 2020, Remote. Sens..

[3]  Mohammad Ali Ghorbani,et al.  GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms , 2020, Geoscience Frontiers.

[4]  Dieu Tien Bui,et al.  A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers , 2019, Geocarto International.

[5]  Biswajeet Pradhan,et al.  Comparison of machine learning models for gully erosion susceptibility mapping , 2020, Geoscience Frontiers.

[6]  Yasin Wahid Rabby,et al.  An investigation of the characteristics, causes, and consequences of June 13, 2017, landslides in Rangamati District Bangladesh , 2020, Geoenvironmental Disasters.

[7]  A-Xing Zhu,et al.  A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods , 2019 .

[8]  A. Zhu,et al.  Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping , 2019, CATENA.

[9]  Yasin Wahid Rabby,et al.  An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping , 2018, Landslides.

[10]  A. Zhu,et al.  Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling , 2018, Bulletin of Engineering Geology and the Environment.

[11]  Chao Zhou,et al.  Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh , 2018, ISPRS Int. J. Geo Inf..

[12]  A-Xing Zhu,et al.  Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. , 2018, The Science of the total environment.

[13]  Mousa Abedini,et al.  Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran , 2018, Environmental Earth Sciences.

[14]  P. Reichenbach,et al.  A review of statistically-based landslide susceptibility models , 2018 .

[15]  Tri Dev Acharya,et al.  Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .

[16]  Muhammad Shafiq,et al.  GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins , 2017 .

[17]  Bayes Ahmed,et al.  Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria , 2017, Journal of Mountain Science.

[18]  Bayes Ahmed,et al.  Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh , 2017, Remote. Sens..

[19]  N. Bhandary,et al.  Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia , 2016, Geoenvironmental Disasters.

[20]  Biswajeet Pradhan,et al.  A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison , 2016 .

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

[22]  Bayes Ahmed,et al.  Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh , 2015, Natural Hazards.

[23]  P. Atkinson,et al.  A systematic review of landslide probability mapping using logistic regression , 2015, Landslides.

[24]  Paul Honeine,et al.  Mahalanobis-based one-class classification , 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

[25]  A. Zhu,et al.  An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic , 2014 .

[26]  Paraskevas Tsangaratos,et al.  Estimating landslide susceptibility through a artificial neural network classifier , 2014, Natural Hazards.

[27]  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 .

[28]  B. Pradhan,et al.  Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya , 2014, Arabian Journal of Geosciences.

[29]  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.

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

[31]  Donatella Caniani,et al.  Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera , 2013 .

[32]  Vicki G. Moon,et al.  Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale , 2012 .

[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]  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..

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

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

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

[38]  V. Doyuran,et al.  A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate , 2004 .

[39]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[40]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[41]  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.

[42]  J. Blahůt,et al.  Spatial agreement of predicted patterns in landslide susceptibility maps , 2011 .

[43]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[44]  David M. Cruden,et al.  LANDSLIDES: INVESTIGATION AND MITIGATION. CHAPTER 3 - LANDSLIDE TYPES AND PROCESSES , 1996 .

[45]  Fausto Guzzetti,et al.  LANDSLIDE HAZARD ASSESSMENT AND RISK EVALUATION: LIMITS AND PROSPECTIVES , 2022 .