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Landslide Susceptibility Mapping Using Probabilistic Frequency Ratio and Shannon Entropy for Chamoli, Uttarakhand Himalayas

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Abstract

In this study, two GIS-based analytical methods, Frequency ratio (FR) and Shannon Entropy (SE), were evaluated for the mapping of the Chamoli region in Uttarakhand, India, for estimating the area's landslide susceptibility. There is a lot of on-going and proposed infrastructure projects in the area due to which, there is a surge in tourism. Thirteen landslide causative factors, namely rainfall, geology, elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), distance to roads, distance to faults/lineaments, distance to river, lithology, annual rainfall, land cover, and geology, are taken into account in this research study. The landslide inventory of 200 landslides was prepared from Bhukosh Portal by Geological Survey of India in point shape file format which are shown as the locations of landslide points. From the findings of this study, two landslide susceptibility maps were created, and they were assessed by using Area Under Curve (AUC) approach of the Receiver Operator Characteristics (ROC) curve by plotting the success rate curve (SRC) and prediction rate curve (PRC). The AUC results showed that the frequency ratio (AUC = 0.923 for SRC and 0.883 for PRC) model is better than the Shannon entropy model (AUC = 0.920 for SRC and 0.877 for PRC) for the predicting landslide susceptibility due to higher AUC values. The maps generated from this research can be extremely useful for the engineers from similar areas for ensuring a safe, disaster resilient infrastructure against a natural disaster like landslides.

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  1. Department of Civil Engineering, Delhi Technological University, Delhi, 110042, India

    Dhruv Bharadwaj & Raju Sarkar

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  1. Dhruv Bharadwaj
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  2. Raju Sarkar
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Correspondence to Raju Sarkar.

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Bharadwaj, D., Sarkar, R. Landslide Susceptibility Mapping Using Probabilistic Frequency Ratio and Shannon Entropy for Chamoli, Uttarakhand Himalayas. Iran J Sci Technol Trans Civ Eng 48, 377–395 (2024). https://doi.org/10.1007/s40996-023-01279-4

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