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

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 thes...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2016-10, Vol.84, p.240-250
Main Authors: Pham, Binh Thai, Pradhan, Biswajeet, Tien Bui, Dieu, Prakash, Indra, Dholakia, M.B.
Format: Article
Language:English
Subjects:
Assessments
Bayesian analysis
Discriminant analysis
India
Landslides
Landslides susceptibility assessment
Logistics
Machine learning
Regression
Support vector machines
Uttarakhand
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Summary: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. •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.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2016.07.005