To develop a real-time evaluation of temporal degradation of the total factor of safety in natural slopes subjected to short-term heavy rainfall, Wakai et al. (2019) have proposed a simple prediction method for groundwater rising in shallow layer of natural slopes. In this study, an infiltration mechanism of fine sands will be analyzed by finite element analysis of saturated-unsaturated seepage. Additionally, a simple method for prediction of infiltrating rainwater in natural slopes in time history will be developed based on the parametric studies with the finite element analysis under the assumption of semi-infinite homogeneous slope.
Publicly released high-resolution 3D topography information could be applied to topographical interpretation which enables to detect landslide-susceptible slopes. However, the burden on engineers or geologists is increasing due to heavy data volume for high-resolution terrain information. In the present study, deep learning was applied to interpretation of landslide topography as a method for efficiently analyzing high-resolution topography information. The deep learning was conducted based on four types of topographic maps such as a contour map, slope map, CS map and color enhancement CS map, and the target data was the Landslide Map in 1 : 50,000 scale published by the National Research Institute for Earth Science and Disaster Resilience. It was found that the locations of the landslide can be identified with a probability of up to 80％ and that accuracy of prediction was highest using a further improved CS map with color enhancement. The application of deep learning to 3D topographical information is effective in supporting the interpretation work and judgment of engineers/geologists and preventing oversight as well.