Landslides in Goa: A Weight of Evidence(WoE) Approach for Mapping

Around the world, landslides are the natural disasters that cause the most devastation and fatalities. By identifying the landslide prone locations in a chosen research area, Landslide Susceptibility Mapping (LSM) assists in reducing the danger of landslides. This article presents the LSM prepared for the state of Goa using Weight of Evidence (WoE) statistical method. Establishing the dimensional relationship linking the historical landslide locations of the research region and the various topographical, hydrological, and geological conditioning elements is necessary for the preparation of LSM utilizing the weight of evidence technique. Information about the 78 historical landslides in the research area was gathered from the publicly accessible Bhukosh portal. Ten landslide conditioning factors have been determined for the research area: slope, elevation, total curvature, plan curvature, profile curvature, yearly rainfall, Stream Power Index (SPI), Topographic Wetness Index (TWI), distance to road, and aspect. The WoE model's input data is values of these thematic variables pertaining to past landslide locations. 20% of this data has been set aside for validating the model's predictive power. The Landslide Susceptibility Index (LSI) is created based on the weights assigned by the WoE algorithm to each causative element. The final map of landslide susceptibility has been classed into susceptibility categories viz., very high, high, moderate, low, and very low. It is found that, the eastern and southern portion of Goa includes localities with more chances of having landslide. Area under the ROC curve is utilized to validate this susceptibility map. This model's validation outcome revealed testing accuracy of 71%. The finding of this study helps in identifying the landslide prone locations of the region of interest.