[H-DS08] Landslides and related phenomena

[HDS08-P02] Landslide Susceptibility Analysis and Mapping in Northern Kyushu Area Using Slope Hazard History and Geological Information

*Daisaku Kawabata¹, Yoshinori MIYACHI¹, Tomoya Abe¹, Makoto Saito¹, Takuya Iyanagi², Hiroki Kobayashi², Hiroyuki Oishi², Kimihiro UCHIMURA², Hiroki Mizuochi¹, Ayumu Miyakawa¹ (1.National Institute of Advanced Industrial Science and Technology, 2.West Japan Engineering Consultants, Inc.)

Keywords:Landslide, Susceptibility Mapping, seamless geological map

The basic factors for landslide occurrence are influenced by multiple conditions such as topography, geology, and vegetation. Spatial information on these factors and past landslide histories can be used to determine which conditions predispose to the occurrence of landslides. In this study, an inventory dataset of topography, geology, and disaster history was compiled mainly for the northern Kyushu region, where heavy rainfall disasters have occurred frequently in recent years. Using 10-m mesh data from the Fundamental Geospatial Data (FGD) by GSI, we developed a topographic inventory of topographic features such as slope and relative relief, and a topographic inventory of watersheds. For the geologic inventory, we used Seamless digital geological map of Japan V2 1:200,000 and classified the geologic divisions into age data and lithologic facies. The same geological features that have been contact metamorphosed by igneous intrusions are harder and less susceptible to collapse. Therefore, the distribution of contact metamorphic zones should be classified in terms of landslide susceptibility and landslide occurrence among geological features of the same type. As data on past landslides to compare with these predispositions, we collected digital archive for landslide distribution map by the National Research Institute for Earth Science and Disaster Resilience (NIED) and information maintained by local governments and organizations and created an inventory data of landslide history.
In this study, using the inventory data, we analyzed the relationship between past landslides and their predisposing factors by dividing them into three categories: rainfall-induced, earthquake-induced, and other factors. In recent years, many studies have also used machine learning and other techniques for hazard mapping. Landslide susceptibility was mapped using multiple methods in this study.