Comparative evaluation of frequency ratio and index of entropy models for landslides susceptibility mapping of Dikhow river basin, Northeast India

Landslide Susceptible Mapping is a significant approach in hilly regions for delineating the potential areas for landslide occurrence. North-eastern states of India are most prone to landslides due to intense monsoonal rainfall, complex geology, and increasing anthropogenic activities. A significant portion of the Dikhow River Basin lies within the hilly regions of Nagaland, where intense monsoonal rainfall and fragile geological conditions trigger slope instability. Numerous reported landslide incidents highlight the severity of this disaster in this region. This research focuses on the comparative assessment of two landslide susceptibility maps generated from two bivariate models, namely (i) Frequency Ratio (FR) and (ii) Index of Entropy (IOE), in the Dikhow River Basin by integrating 12 landslide triggering factors and 632 landslide inventories using Geospatial Technology. The generated Landslide Susceptibility Map has been categorized into five hierarchical classes, from very low to very high, using the natural-break classification system. The study indicated that 19.9% and 8.4% of the study area correspond to very high susceptibility to landslides by FR and IOE, respectively. The validation results illustrated satisfactory performance for both models with AUC values of 0.81 and 0.79 for the training datasets, and 0.82 and 0.8 for the testing datasets, respectively. R-index values indicate that 64.81% (FR) and 60.25% (IOE) of landslide occurrences were associated with very high susceptibility classes. The validation process reveals that the FR has better success in comparison to the IOE model. In order to reduce the negative effects of landslide hazards, the suggested research will assist government, policymakers, and decision-makers in managing and mitigating the adverse consequences of landslides.