In recent years, sediment disaster has frequently been caused by heavy rainfall and has cost many human lives and great property losses. To estimate such risks, Wakai et al. [1] proposed a simplified prediction method to calculate the variation of groundwater levels in natural slopes both at the time of rainfall in wide areas and in real time. To calculate the variation of groundwater levels using this method, the slope conditions (such as material constant and initial conditions) must be determined in advance. This study takes the 2017 heavy rainfall in Northern Kyushu as an example to analyze surface layer thickness, one of the slope conditions that most significantly influences slope stability, over wide areas. The findings reveal that the prediction of slope failure distribution differs depending on how the surface layer thickness and sliding surface are determined.
To fully and rapidly develop a real-time early warning judgment system for slope failure at the time of heavy rains including overseas, it is necessary to predict water movement in the soil at the time of rainfall. In addition, to apply the system to a place where insufficient geotechnical and geological data have been amassed, it is necessary to evaluate the risk of slope failure based on physical properties obtained from a simple soil test. Therefore, in this study, the authors set Gogoshima Island in Ehime Prefecture as a study site and evaluated the water movement over time in the soil during heavy rain using a simple prediction equation of rainfall seepage process. Soil properties were determined through simple in-situ and laboratory tests. As a result, it was found that the factor of safety for slope failure in the head and wall of a valley dissecting the hillside slope composed of granodiorite in which weathering has progressed can be planarly evaluated using the simple prediction equation.
Measuring the amount of rainfall is essential for a wide-area evaluation of the risk of landslide disaster using a real-time simulation. In Thailand, located in Monsoon Asia, point observation is conducted using a rain gauge. Interpolation calculation is crucial for obtaining the planar rainfall intensity for the wide-area analysis from scattered point observation data. In this study, to accurately calculate rainfall intensity using the inverse distance weighting (IDW) method, the parameters affecting the results are examined. Additionally, using obtained rainfall data, a simple prediction calculation of groundwater level fluctuation by Wakai et al. [1] and Ozaki et al. [2] is performed. Finally, the relationship between the rainfall intensity and the fluctuation of groundwater level will be discussed.
Hau Thao Village is located in Sa Pa Prefecture, in northern Vietnam. The village contains one of the most picturesque landscapes with terraced paddy fields located in landslide topography formed on a gentle slope. However, the creation of the topography has not been sufficiently clarified. In this study, samples of soil and stone are taken from two landslide areas in Hau Thao Village for mineral composition analysis, clarifying that the sauce rock of the deposits comprising the landslide areas is made up of granitoids, forming the upper slope above the fault located in the hinterland. The landslides occurring in Hau Thao Village are caused by the remobilization of the secondary deposits transported from the upper part of the slope by debris flow.
In recent years, airborne laser scanning has been used for terrain surveys of broad areas in Japan. This study attempted to extract the landslide-prone slope based on geomorphological and slope stability analyses using Digital Elevation Model obtained by airborne laser scanning. The study site is located in the mountainous region of the Shikoku Mountains, where landslides occur on the gentle slope deformed by mass rock creeps. Implementing slope stability analysis to incorporate “potential to increase pore water pressure” found that landslides occur in areas with low factor of safety. In the future, it is expected that the method developed in this study could contribute to the planning of basin-based disaster management.
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