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Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology

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Abstract

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

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Acknowledgements

The authors are thankful to the Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, for giving the adequate environment for the research work.

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  1. Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India

    Abhik Saha, Lakshya Tripathi & Vasanta Govind Kumar Villuri

  2. Research Project Monitoring Department, Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun, 248001, India

    Ashutosh Bhardwaj

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Abhik Saha and Lakshya Tripathi under the supervision of Vasanta Govind Kumar Villuri and Ashutosh Bhardwaj. The first draft of the manuscript was written by Abhik Saha, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Vasanta Govind Kumar Villuri.

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Saha, A., Tripathi, L., Villuri, V.G.K. et al. Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology. Environ Sci Pollut Res 31, 10443–10459 (2024). https://doi.org/10.1007/s11356-023-31670-7

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