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Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

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Abstract

Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand state, India, an area affected by numerous landslides causing significant losses of life, infrastructure and property every year. Decision tree-based machine learning methods, namely Random Forest (RF), Logistic Model Trees (LMT), Best First Decision Trees (BFDT) and Classification and Regression Trees (CART) have been used, and results are compared herein for proper spatial prediction of landslides. Analysis of the data has been done considering sixteen conditioning factors (i.e., slope angle, elevation, slope aspect, profile curvature, land cover, curvature, lithology, plan curvature, soil, distance to lineaments, lineament density, distance to roads, road density, distance to river, river density and rainfall), and 1295 historical landslide polygons. Models were validated and compared using Receiver Operating Characteristics (ROC) curve and statistical indices. The results show that the RF model has the highest predictive capability, followed by the LMT, BFDT and CART models, respectively, and indicate that although all four methods have shown good results, the performance of the RF method is the best for landslide spatial prediction.

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Acknowledgements

The authors are thankful to the Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science & Technology, Government of Gujarat, Gandhinagar, Gujarat, India, for providing facilities to carry out this research work.

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  1. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam

    Binh Thai Pham

  2. Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari, Iran

    Khabat Khosravi

  3. Department of Science & Technology, Government of Gujarat, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Gandhinagar, India

    Indra Prakash

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  1. Binh Thai Pham
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Correspondence to Binh Thai Pham.

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Pham, B.T., Khosravi, K. & Prakash, I. Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India. Environ. Process. 4, 711–730 (2017). https://doi.org/10.1007/s40710-017-0248-5

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