This study proposes a method for the identification of locations for which risk of slope failure during heavy rain is high. Using teaching data created from areas of slope failure during heavy rain, a prediction model is created to assess slope failure risk for individual detailed digital elevation models (DEMs). This prediction model achieves a12％ improvement in accuracy over the conventional technique. Appropriate assessments are also conducted for regions in which prediction has been difficult in the past. In addition, this study has succeeded in simplifying the structure of the deep neural network (DNN) through the collection of high-quality teaching data. Based on this method of analysis, a method of assessing the degree of risk for mountain streams is proposed.

  This study analyzes the influence of surface water and groundwater flows on a landslide in Takumi area, Tomioka City, Gunma Prefecture induced by Typhoon Hagibis on 12 October 2019. Firstly, a surface infiltration model and surface runoff simulation module were integrated into a water table prediction model developed previously. Then the hydrological integration process was validated on a tilted impermeable V-catchment system and trialed on a Plexiglas sandbox system. Finally, the extended model was used to describe the infiltration and exfiltration process, surface water flow as well as groundwater level distribution for a study area in Takumi Village during the Typhoon. According to simulation results, both surface water and groundwater flows have gathered on the landslide sites where the slopes are concave. Surface water and groundwater have interacted with each other to form flows over time and cause the groundwater level in the study area to rise. At the time of the landslide, the groundwater level had reached the slope surface in most of the cells at the collapsed sites. As a result, the safety factor for slopes decreased significantly and landslides occurred. Therefore, both surface water and groundwater flows were thought to be the landslide triggers.