Application of the SHALSTAB model for the identification of areas susceptible to landslides: Brazilian case studies.

Abstract

Since the 1960s, catastrophic and generalized events of hazardous mass movements caused millions of dollars in economic losses and resulted in thousands of fatalities and homelessness in Brazil. To understand these processes and attempt to predict them, mathematical models have been utilized world-wide describing the physics of the process through mathematical equations. The objective of this study was to present two areas widely affected by shallow landslides where the SHALSTAB model was applied to understand the process and to predict potentially unstable areas in several hydrographic basins. Simulations utilized the types of distinct data that were available in each area. From both areas, geotechnical data collected in the field, topographical data from digital topographical maps and Digital Terrain Models (DTM) from Light Detection and Ranging (LiDAR) were utilized. Susceptibility maps were validated using two indexes, scar concentration (SC) and landslide potential (LP), based on landslides that occurred in 1985 and 2011. Both indexes showed satisfactory results given that the unconditionally unstable category described more than 45% of the landslide events, and the LP index displayed the highest values for the most unstable categories.