Landslide Susceptibility Mapping using Statistical Information Value Model: A Case Study of part of Chamoli District, Uttarakhand India

Landslides have become increasingly frequent and destructive in Uttarakhand, leading to substantial loss of life and significant damage to infrastructure. This research focuses on generating a detailed landslide susceptibility map for a selected area in Chamoli district, Uttarakhand, by integrating remote sensing and geographical information system (GIS) techniques. Twelve critical factors influencing landslide occurrence, such as slope, aspect, vegetation cover, proximity to geological structures, distance from roads, elevation, curvature, topographic wetness index (TWI), stream power index (SPI), drainage proximity, and lithology, were considered. The Statistical Information Value Model (SIVM) was used to assess the contribution (weight) of each factor class toward landslide occurrence. These derived weights were then integrated using a weighted overlay method to produce the final landslide susceptibility map. The predictive accuracy of the model was validated through receiver operating characteristic (ROC) analysis, achieving an area under the curve (AUC) value of 0.72. The results demonstrate that the SIVM-based weighted overlay approach provides a reliable tool for identifying landslide-prone zones, offering valuable insights for land use planning and disaster mitigation.