Landslide susceptibility mapping using a bivariate statistical model in a tropical hilly area of southeastern Brazil

Abstract:

Landslides are the most damaging natural hazard in the mountainous region of Rio de Janeiro State in Brazil, responsible for thousands of deaths and important financial and environmental losses. However, this region has currently few landslide susceptibility maps implemented on an adequate scale. Identification of landslide susceptibility areas is fundamental in successful land use planning and management practices to reduce risk. This paper applied the Bayes’ theorem based on weight of evidence (WoE) using 8 landslide-related factors in a geographic information system (GIS) for landslide susceptibility mapping. 378 landslide locations were identified and mapped on a selected basin in the city of Nova Friburgo, triggered by the January 2011 rainfall event. The landslide scars were divided into two subsets: training and validation subsets. The 8 landslide-related factors weighted by WoE were performed using chi-square test to indicate which variables are conditionally independent of each other to be used in the final map. Finally, the maps of weighted factors were summed up to construct the landslide susceptibility map and validated by the validation landslide subset. According to the results, slope, aspect and contribution area showed the higher positive spatial correlation with landslides. In the landslide susceptibility map, 21% of the area presented very low and low susceptibilities with 3% of the validation scars, 41% presented medium susceptibility with 22% of the validation scars and 38% presented high and very high susceptibilities with 75% of the validation scars. The very high susceptibility class stands for 16% of the basin area and has 54% of the all scars. The approach used in this study can be considered very useful since 75% of the area affected by landslides was included in the high and very high susceptibility classes.