Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods
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Biswajeet Pradhan | Maher Ibrahim Sameen | Hyuck-Jin Park | Jung Hyun Lee | M. I. Sameen | B. Pradhan | Hyuck-Jin Park
[1] Wolfgang Kresse,et al. Springer Handbook of Geographic Information , 2012, Springer Handbooks.
[2] G. Montana,et al. Mount Etna volcano (Italy) as a major “dust” point source in the Mediterranean area , 2016, Arabian Journal of Geosciences.
[3] H. Pourghasemi,et al. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran , 2016, Environmental Earth Sciences.
[4] 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.
[5] Nizamettin Aydin,et al. A novel gene selection algorithm for cancer identification based on random forest and particle swarm optimization , 2015, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).
[6] 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..
[7] Alexander Brenning,et al. Modelling Landslide Susceptibility for a Large Geographical Area Using Weights of Evidence in Lower Austria, Austria , 2015 .
[8] Q. Cheng,et al. Conditional Independence Test for Weights-of-Evidence Modeling , 2002 .
[9] 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..
[10] 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.
[11] L. Ayalew,et al. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .
[12] Peijun Du,et al. Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[13] Suzana Dragicevic,et al. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments , 2015 .
[14] Deepak Kumar,et al. Landslide Susceptibility Mapping & Prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India , 2017 .
[15] P. Griffin,et al. Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections , 2015, Epidemiology and Infection.
[16] Thomas Blaschke,et al. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping , 2014, Comput. Geosci..
[17] Umi Kalthum Ngah,et al. Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network , 2013, TheScientificWorldJournal.
[18] Dino Bindi,et al. Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan , 2015, Bulletin of Engineering Geology and the Environment.
[19] M. Rossi,et al. Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities , 2012 .
[20] J. McCalpin,et al. Producing landslide-susceptibility maps for regional planning in data-scarce regions , 2012, Natural Hazards.
[21] P. Groenen,et al. The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics , 2015, BioMed research international.
[22] Liangjie Wang,et al. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network , 2016, Geosciences Journal.
[23] Antonio Francipane,et al. Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping , 2016, Environ. Model. Softw..
[24] Hamid Reza Pourghasemi,et al. Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran , 2016, Arabian Journal of Geosciences.
[25] Poonam,et al. Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini valley, (central Himalaya), for disaster governance using the Weights of Evidence method , 2017 .
[26] 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.
[27] D. R. Cutler,et al. Utah State University From the SelectedWorks of , 2017 .
[28] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[29] Vincent Baeten,et al. Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .
[30] P. Reichenbach,et al. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model , 2016 .
[31] G. Heuvelink,et al. A generic framework for spatial prediction of soil variables based on regression-kriging , 2004 .
[32] Ebru Akcapinar Sezer,et al. A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments , 2013, Comput. Geosci..
[33] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[34] 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 .
[35] Bo Du,et al. Target Detection Based on Random Forest Metric Learning , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[36] Saro Lee,et al. Landslide susceptibility analysis and verification using the Bayesian probability model , 2002 .
[37] 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.
[38] G. Bonham-Carter. Geographic Information Systems for Geoscientists: Modelling with GIS , 1995 .
[39] 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.
[40] Ronald M. Summers,et al. Automated segmentation of the thyroid gland on CT using multi-atlas label fusion and random forest , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[41] Nachiappan Subramanian,et al. A review of applications of Analytic Hierarchy Process in operations management , 2012 .
[42] George L. W. Perry,et al. Identifying the controls on coastal cliff landslides using machine-learning approaches , 2016, Environ. Model. Softw..
[43] Birgit Terhorst,et al. Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany) , 2007 .
[44] 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 .
[45] M. Joseph,et al. Biochemical and stable carbon isotope records of mangrove derived organic matter in the sediment cores , 2016, Environmental Earth Sciences.
[46] D. Altman,et al. Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.