A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). (October 2016)

• Record Type: Journal Article
• Authors: Pham, Binh Thai; Pradhan, Biswajeet; Tien Bui, Dieu; Prakash, Indra; Dholakia, M.B.
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 240
• Page End: 250
• Publication Date: 2016-10
• Subjects: Landslides susceptibility assessment -- Machine learning -- Uttarakhand -- India; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.07.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910–0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively. Highlights: Machine learning methods namely SVM, LR, FLDA, BN, and NB have been evaluated and compared for landslide susceptibility assessment. Results indicate that all these five models can be applied efficiently for landslide assessment and prediction. Analysis of comparative results reaffirmed that the SVM model is one of the best methods.