Mapping landslide susceptibility and types using Random Forest

Abstract

Landslides are one of the most destructive natural hazards; they can drastically alter landscape morphology, destroy man-made structures, and endanger people’s life. Landslide susceptibility maps (LSMs), which show the spatial likelihood of landslide occurrence, are crucial for environmental management, urban planning, and minimizing economic losses. To date, the majority of research into data mining LSM uses small-scale case studies focusing on a single type of landslide. This paper presents a data mining approach to producing LSM for a large, heterogeneous region that is susceptible to multiple types of landslides. Using a case study of Piedmont, Italy, a Random Forest algorithm is applied to produce both susceptibility maps and classification maps. These maps are combined to give a highly accurate (over 85% classification accuracy) LSM which contains a large amount of information and is easy to interpret. This novel method of mapping landslide susceptibility demonstrates the efficacy of Random Forest to produce highly accurate susceptibility maps for a large heterogeneous region without the need for multiple susceptibility assessments.