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Application of Classification and Regression Trees for Spatial Prediction of Rainfall-Induced Shallow Landslides in the Uttarakhand Area (India) Using GIS

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Climate Change, Extreme Events and Disaster Risk Reduction

Part of the book series: Sustainable Development Goals Series ((SDGS))

Abstract

Landslide is defined to be a mass movement of slope materials from up- to downslope under various geo-environmental conditions. It is well known as one of the most serious geo-hazards causing loss of human life and properties throughout the world. In the present study, we present an application of Classification and Regression Trees (CART) for spatial prediction of rainfall-induced shallow landslides in the Uttarakhand area (India) using GIS. A total of 430 historical landslide locations have been first identified to construct landslide inventory map. In addition, eleven landslide influencing factors (slope angle, slope aspect, elevation, curvature, lithology, soil type, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall) have been taken into account for analyzing the spatial relationship with landslide occurrences. Moreover, the predictive capability of the CART model has been validated using statistical analysis-based evaluations. Overall, the CART model performs well for spatial prediction of landslides. Its performance is even better than other landslide models (Naïve Bayes and Naïve Bayes Trees). Therefore, the CART indicates as encouraging alternative method which could be used for landslide prediction in landslide-prone areas. The results obtained from this study would be helpful for landslide preventing and combating activities in the study area.

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Acknowledgements

Authors would like to sincerely thank Director, Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India, for providing facilities to carry out this research work.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Gujarat Technological University, Nr.Visat Three Roads, Visat-Gandhinagar Highway, Chandkheda, Ahmedabad, 382424, Gujarat, India

    Binh Thai Pham

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

    Binh Thai Pham

  3. Geographic Information System Group, Department of Business Administration and Computer Science, Telemark University College, Hallvard Eikas Plass 1, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

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

    Indra Prakash

Authors
  1. Binh Thai Pham
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  2. Dieu Tien Bui
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  3. Indra Prakash
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Corresponding author

Correspondence to Binh Thai Pham .

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Editors and Affiliations

  1. Department of Geography, Shaheed Bhagat Singh College, University of Delhi, New Delhi, India

    Suraj Mal

  2. Department of Geography, Delhi School of Economics, University of Delhi, New Delhi, India

    R.B. Singh

  3. Department of Geography, University of Zurich—Irchel, Zürich, Switzerland

    Christian Huggel

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Pham, B.T., Tien Bui, D., Prakash, I. (2018). Application of Classification and Regression Trees for Spatial Prediction of Rainfall-Induced Shallow Landslides in the Uttarakhand Area (India) Using GIS. In: Mal, S., Singh, R., Huggel, C. (eds) Climate Change, Extreme Events and Disaster Risk Reduction. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-319-56469-2_11

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