期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 84, 期 -, 页码 240-250出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2016.07.005
关键词
Landslides susceptibility assessment; Machine learning; Uttarakhand; 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 Naive 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. (C) 2016 Elsevier Ltd. All rights reserved.
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