Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
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Nadhir Al-Ansari | Himan Shahabi | Ataollah Shirzadi | Baharin Bin Ahmad | Ayub Mohammadi | Abolfazl Jaafari | Viet-Ha Nhu | Wei Chen | John J Clague | J. Clague | Wei Chen | H. Shahabi | B. Ahmad | A. Jaafari | A. Shirzadi | Viet-Ha Nhu | N. Al‐Ansari | Hoang Nguyen | Hoang Nguyen | A. Mohammadi
[1] A. Clerici,et al. A procedure for landslide susceptibility zonation by the conditional analysis method , 2002 .
[2] Filippo Catani,et al. Landslide susceptibility map refinement using PSInSAR data , 2016 .
[3] C. F. Lee,et al. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .
[4] Nadhir Al-Ansari,et al. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam , 2020, International journal of environmental research and public health.
[5] Wei Chen,et al. GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques , 2019, Applied Sciences.
[6] P. T. Ghazvinei,et al. A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers , 2020 .
[7] Himan Shahabi,et al. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment , 2018, Geocarto International.
[8] Himan Shahabi,et al. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability , 2019, Agricultural and Forest Meteorology.
[9] 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..
[10] 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 .
[11] Himan Shahabi,et al. INTEGRATION OF INSAR TECHNIQUE, GOOGLE EARTH IMAGES AND EXTENSIVE FIELD SURVEY FOR LANDSLIDE INVENTORY IN A PART OF CAMERON HIGHLANDS, PAHANG, MALAYSIA , 2018 .
[12] Dieu Tien Bui,et al. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. , 2019, Journal of environmental management.
[13] Joseph H. A. Guillaume,et al. Characterising performance of environmental models , 2013, Environ. Model. Softw..
[14] Bayes Ahmed,et al. Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh , 2017, Remote. Sens..
[15] Ataollah ShirzadiLee. A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran , 2012 .
[16] D. Bui,et al. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach , 2017, Environmental Earth Sciences.
[17] A. Jaafari. LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process , 2018, Environmental Earth Sciences.
[18] 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 .
[19] B. Pham,et al. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran , 2019, Sustainability.
[20] A. Darvishsefat,et al. Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran) , 2019, Journal of Forestry Research.
[21] 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..
[22] T. Brewer,et al. Rainfall thresholding and susceptibility assessment of rainfall-induced landslides: application to landslide management in St Thomas, Jamaica , 2009 .
[23] Saro Lee,et al. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. , 2019, The Science of the total environment.
[24] The sand dunes migration patterns in Mesr Erg region using satellite imagery analysis and wind data , 2017 .
[25] John J. Clague,et al. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran , 2020 .
[26] B. Pradhan,et al. Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles , 2014 .
[27] Wei Chen,et al. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility , 2019, CATENA.
[28] Dieu Tien Bui,et al. Spatially explicit predictions of changes in the extent of mangroves of Iran at the end of the 21st century , 2020 .
[29] D. Bui,et al. Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling , 2019, Forests.
[30] Binh Thai Pham,et al. Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers , 2018, Ecol. Informatics.
[31] 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 .
[32] Nadhir Al-Ansari,et al. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier , 2020, Remote. Sens..
[33] Dieu Tien Bui,et al. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility , 2019, CATENA.
[34] Dieu Tien Bui,et al. Hybrid computational intelligence models for groundwater potential mapping , 2019, CATENA.
[35] 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.
[36] Wei Chen,et al. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping , 2019, Water.
[37] Hossein Moayedi,et al. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility , 2020 .
[38] Mohammad Mehrabi,et al. Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide , 2019, Geomatics, Natural Hazards and Risk.
[39] Biswajeet Pradhan,et al. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping , 2018, Sensors.
[40] A. Zhu,et al. A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China) , 2017, Environmental Earth Sciences.
[41] Zahra Kalantari,et al. Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia. , 2019, The Science of the total environment.
[42] Jie Dou,et al. New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed , 2019, Forests.
[43] 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 .
[44] D. Bui,et al. 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 , 2017, International Journal of Sediment Research.
[45] Sahana,et al. Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms , 2019, Sustainability.
[46] Majid Shadman Roodposhti,et al. Landslide susceptibility mapping using geographically-weighted principal component analysis , 2014 .
[47] Wei Chen,et al. Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping , 2020, Symmetry.
[48] Saro Lee,et al. Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea , 2003 .
[49] A. Zhu,et al. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method , 2018 .
[50] F. Dai,et al. Assessment of land-slide susceptibility on the natural terrain of Lantau Island , 2001 .
[51] Javad Rezaeian,et al. Prediction of Slope Failures in Support of Forestry Operations Safety , 2017 .
[52] Nadhir Al-Ansari,et al. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms , 2020, International journal of environmental research and public health.
[53] Wei Chen,et al. GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models , 2020, Applied Sciences.
[54] Biswajeet Pradhan,et al. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. , 2019, The Science of the total environment.
[55] Binh Thai Pham,et al. Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran , 2019, Comput. Electron. Agric..
[56] H. Shahabi,et al. Soil Erosion Hazard Mapping in Central Zab Basin Using Epm Model in GIS Environment , 2016 .
[57] Nadhir Al-Ansari,et al. Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models , 2020, Water.
[58] 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.
[59] Bahareh Kalantar,et al. Two novel neural-evolutionary predictive techniques of dragonfly algorithm (DA) and biogeography-based optimization (BBO) for landslide susceptibility analysis , 2019, Geomatics, Natural Hazards and Risk.
[60] Dieu Tien Bui,et al. Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics , 2019, Remote. Sens..
[61] Lee Saro,et al. A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran , 2012, Natural Hazards.
[62] A. Jaafari,et al. Modeling erosion and sediment delivery from unpaved roads in the north mountainous forest of Iran , 2015 .
[63] Mazlan Hashim,et al. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment , 2015, Scientific Reports.
[64] Mustafa Neamah Jebur,et al. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale , 2014 .
[65] Philippa J. Mason,et al. Landslide hazard assessment in the Three Gorges area of the Yangtze river using ASTER imagery: Zigui–Badong , 2004 .
[66] Biswajeet Pradhan,et al. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm , 2019, Remote. Sens..
[67] Yoav Freund,et al. The Alternating Decision Tree Learning Algorithm , 1999, ICML.
[68] Wei Chen,et al. Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia , 2018, Remote. Sens..
[69] Dieu Tien Bui,et al. Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction , 2019, Applied Sciences.
[70] R. Danby,et al. Inconsistent relationships between annual tree ring-widths and satellite-measured NDVI in a mountainous subarctic environment , 2018, Ecological Indicators.
[71] H. Shahabi,et al. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. , 2018, Journal of environmental management.
[72] M. Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .
[73] B. Pradhan,et al. A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods , 2019, Journal of Hydrology.
[74] Lior Rokach,et al. Ensemble-based classifiers , 2010, Artificial Intelligence Review.
[75] Biswajeet Pradhan,et al. Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis , 2019, Natural Resources Research.
[76] Wei Chen,et al. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. , 2018, The Science of the total environment.
[77] Wei Chen,et al. Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping , 2020 .
[78] Ravinesh C. Deo,et al. Land subsidence modelling using tree-based machine learning algorithms. , 2019, The Science of the total environment.
[79] M. Turrini,et al. An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy) , 2002 .
[80] T. Cheng,et al. Mapping landslide susceptibility and types using Random Forest , 2018 .
[81] Nadhir Al-Ansari,et al. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment , 2020, Water.
[82] 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.
[83] Himan Shahabi,et al. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. , 2019, The Science of the total environment.
[84] Abolfazl Jaafari,et al. Mangrove regional feedback to sea level rise and drought intensity at the end of the 21st century , 2020 .
[85] 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..
[86] D. Bui,et al. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution , 2019, CATENA.
[87] Hossein Shafizadeh-Moghadam,et al. Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran , 2018, Earth Science Informatics.
[88] Wei Chen,et al. Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree , 2019, Geocarto International.
[89] H. Shahabi,et al. Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran , 2015, Environmental Earth Sciences.
[90] D. Bui,et al. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches , 2019, CATENA.
[91] Nadhir Al-Ansari,et al. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping , 2020, Applied Sciences.
[92] Dieu Tien Bui,et al. A comparative study between popular statistical and machine learning methods for simulating volume of landslides , 2017 .
[93] Wei Chen,et al. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China , 2017 .
[94] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[95] Haijun Wang,et al. Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China , 2012 .
[96] Wei Chen,et al. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms , 2018, Sensors.
[97] Yanli Wu,et al. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping , 2020 .
[98] T. Topal,et al. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey) , 2003 .
[99] David J. Wachal,et al. Mapping landslide susceptibility in Travis County, Texas, USA , 2000 .
[100] M. Komac. A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia , 2006 .
[101] Yi Wang,et al. Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , 2019, Applied Sciences.
[102] Wei Chen,et al. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods , 2018, Natural Hazards.
[103] 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 .
[104] B. Pradhan,et al. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia , 2010 .
[105] Ali P. Yunus,et al. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan , 2019, Landslides.
[106] 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..
[107] A Mohammadi,et al. LAND COVER MAPPING USING A NOVEL COMBINATION MODEL OF SATELLITE IMAGERIES: CASE STUDY OF A PART OF THE CAMERON HIGHLANDS, PAHANG, MALAYSIA , 2019, Applied Ecology and Environmental Research.
[108] L. Tham,et al. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .
[109] Francisco Gutiérrez,et al. Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms , 2019, Land Degradation & Development.
[110] Biswajeet Pradhan,et al. Hybridized neural fuzzy ensembles for dust source modeling and prediction , 2020 .
[111] B. Pham,et al. Bagging based Support Vector Machines for spatial prediction of landslides , 2018, Environmental Earth Sciences.
[112] Abolfazl Jaafari,et al. Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran. , 2019, The Science of the total environment.
[113] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..