Landslide Susceptibility Mapping by Using Logistic Regression Model with Neighborhood Analysis: A Case Study in Mizunami City

Abstract

Landslides which affect human lives and economic losses are always attracted a lot of concerning in modern society. In order to identify the potential hazardous areas related to landslides, three methods have been used, such as qualitative or knowledge-based method, deterministic method and quantitative-based method. Geographical information system (GIS) technology and high computing ability provide a convenient tool to deal with landslide triggering factors and make the quantitative-based method achieve effectively.

In this study, landslide-related factors such as topographical elevation, slope angle, slope aspect, topographical wetness index (TWI) and stream power index (SPI), were employed in the landslide susceptibility analysis. The logistical regression was used to obtain the relationships for landslide susceptibility between landslides and causative factors. The distributions of observed landslides were used to evaluate the performance of the susceptibility map. The approaches described in this paper showed us that the logistical regression and neighborhood can be used as simple tools to predict the potential landslide locations. This map will be helpful for city planning, infrastructure construction and agriculture developments in the future.