Indian Journal of Science and Technology
DOI: 10.17485/ijst/2018/v11i12/99745
Year: 2018, Volume: 11, Issue: 12, Pages: 1-10
Original Article
Binh Thai Pham1* and Indra Prakash2
1 Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam; [email protected]
2 Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar - 382421, Gujarat, India; [email protected]
*Author for correspondence
Binh Thai Pham,
Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam; [email protected]
Objectives: To evaluate performance of machine learning methods for assessment of landslide susceptibility at Himalayan area, India. Methods/Statistical analysis: Machine learning methods namely Kernel Logistic Regression (KLR) and Classification and Regression Trees (CART) were applied and compared in this study. Landslide affecting parameters and 930 historical landslides were used for generating datasets. Receiver Operating Characteristic (ROC) curve and Statistical analysis methods were used for validation and comparison. Findings: Result analysis shows that both the KLR and CART models perform well for landslide susceptibility assessment but the KLR model (AUC = 0.894) outperforms the CART model (AUC = 0.842). Thus, both these methods can be considered as promising machine learning techniques for landslide susceptibility assessment; however, the KLR is better than the CART. Application/Improvements: Results of this study would be useful for susceptibility assessment and landslide hazard management in landslide prone areas.
Keywords: Classification and Regression Trees (CART), Kernel Logistic Regression (KLR), Landslides, Machine Learning
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