A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment

Springer Science and Business Media LLC - Tập 78 Số 3 - Trang 1911-1925 - 2019
Binh Thai Phạm1, Indra Prakash2
1Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam
2Department of Science & Technology, Government of Gujarat, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Gandhinagar, India

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Tài liệu tham khảo

Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54:1127–1143

Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135

Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81

Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River basin case study, Italy. Mathematical geosciences 44:47–70

Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6:2812–2831

Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343

Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10:122

Freund Y, Schapire RE A 1995. Desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory, Springer, pp 23–37

Frye C (2007) About the Geometrical Interval classification method http://blogsesri.com/esri/arcgis

Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning

Hsieh N-C, Hung L-P (2010) A data driven ensemble classifier for credit scoring analysis. Expert Syst Appl 37:534–545

Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

Kira K, Rendell LA 1992. A practical approach to feature selection. In: Proceedings of the ninth international workshop on Machine learning, pp 249–256

Kohavi R 1996. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: KDD, pp 202–207

Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Hoboken

Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology 50:847–855

Lee S, Hwang J, Park I (2013) Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena 100:15–30

Li C 2007. Classifying imbalanced data using a bagging ensemble variation (BEV). In: Proceedings of the 45th annual southeast regional conference, ACM, pp 203–208

Michalski RS, Carbonell JG, Mitchell TM (2013) Machine learning: an artificial intelligence approach. Springer Science & Business Media, Berlin

Murphy KP (2006) Naive bayes classifiers University of British Columbia

NCEP (2014) Global weather data for SWAT http://globalweathertamu.edu/home

Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) 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. Theor Appl Climatol 122:1–19. https://doi.org/10.1007/s00704-015-1702-9

Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016a) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250. https://doi.org/10.1016/j.envsoft.2016.07.005

Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016b) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards 83:1–31. https://doi.org/10.1007/s11069-016-2304-2

Pham BT, Bui DT, Prakash I (2017a) Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees, Logistic Regression and J48 Decision Trees Methods: A Comparative Study Geotechnical and Geological Engineering:1–15

Pham BT, Khosravi K, Prakash I (2017b) Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal area. Uttarakhand, India Environmental Processes, pp 1–20

Pham BT, Tien Bui D, Prakash I, Dholakia MB (2017c) Hybrid integration of multilayer Perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149(Part 1):52–63. https://doi.org/10.1016/j.catena.2016.09.007

Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron highlands, Malaysia). IEEE Trans Geosci Remote Sens 48:4164–4177

Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology 115:172–187

Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28:1619–1630

Seni G, Elder JF (2010) Ensemble methods in data mining: improving accuracy through combining predictions. Synthesis Lectures on Data Mining and Knowledge Discovery 2:1–126

Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications 38:8208–8219

Shirzadi A et al (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences 76:60

Tallarida RJ, Murray RB (1987) Chi-square test. In: Manual of Pharmacologic Calculations. Springer, pp 140–142

Tien Bui D, Ho T-C, Pradhan B, Pham B-T, Nhu V-H, Revhaug I (2016a) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, bagging, and MultiBoost ensemble frameworks. Environmental Earth Sciences 75:1–22. https://doi.org/10.1007/s12665-016-5919-4

Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016b) 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. Landslides 13:361–378

Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and naive Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 145:164–179

Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York

Wang Y, Makedon F 2004. Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data. In: Computational Systems Bioinformatics Conference, CSB 2004. Proceedings. 2004 IEEE, 2004. IEEE, pp 497–498

Wang H, Khoshgoftaar TM, Napolitano A (2012) Software measurement data reduction using ensemble techniques. Neurocomputing 92:124–132

Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40:159–196

West D, Mangiameli P, Rampal R, West V (2005) Ensemble strategies for a medical diagnostic decision support system: a breast cancer diagnosis application. Eur J Oper Res 162:532–551

Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52