LANDSLIDE SUSCEPTIBILITY ASSESMENT IN SIKKIM USING FREQUENCY RATIO AND SHANNON ENTROPY APPROACH

Abstract

Landslide Susceptibility Assessment (LSA) is crucial for understanding and mitigating landslide risks, especially in vulnerable regions like Sikkim, where complex topography, diverse geological formations, and significant precipitation contribute to instability. This study integrates geospatial analysis with statistical modelling techniques to evaluate the influence of 10 landslide conditioning factors (LCFs), including aspect, rainfall, curvature, elevation, hillshade, roughness, slope, distance to stream and topographic wetness index (TWI) and normalized difference vegetation index (NDVI). Using a landslide inventory of 677 recorded events and GIS-derived thematic layers were used to apply two statistical models: Frequency Ratio (FR) and Shannon Entropy (SE). Landslide inventory is created by dividing the total landslide points into training data (80%) and testing data (20%). The vulnerability map is then created using the 2 models and a comparison is done between them. Then, the map created by the above-mentioned 2 models is validated with the help of a testing data set using the AUC (Area under curve) of the ROC (Receiver Operating Characteristics) curve. The resulting susceptibility map categorizes the state into distinct risk zones, aiding in hazard mitigation and land-use planning. The Frequency Ratio shows AUC of 0.760 and the Shannon Entropy shows the AUC of 0.724. Model validation through AUC- ROC analysis and success prediction rate curves confirm the reliability and predictive accuracy of the applied techniques. This comparative study helps in underscores the importance of combining statistical models with geospatial tools for disaster risk management and infrastructure planning in Sikkim. These maps can be further utilized by the government in mitigation planning.