In this study, we consider the debris flow trap function of the open-type steel sabo dam using channel experiments. Various studies have been conducted on the sediment trap function of open-type sabo dams due to boulder blocking in steep-slope areas, considered as areas with a slope of 15°or greater. However, many open-type sabo dams have been built in moderate-slope areas, which have a slope less than 15°. Few studies have been conducted on open-type sabo dams built in mild-slope areas. Therefore, we consider the trap function of the open-type sabo dams in areas having slopes of 12, 9, and 6°. In addition, we consider different shapes of the open-type steel sabo dam : grid-type, vertical-type, and horizontal-type. The results show that the trap functions were effective for all slope cases and for all types of dams. Steeper slopes resulted in higher trap functions. The grid-type and vertical-type dams trapped particles more effectively, with higher trap functions than the horizontal-type dams, and large differences appeared in cases where the slope was 6°.
Extensive research on landslide susceptibility and landslide-affected areas has been conducted, but many technologies still lack sufficient accuracy and information to predict the movement of collapsed material. Adequate disaster mitigation requires prediction of the movement type and travel distance of collapsed material from deep-seated landslides. This research aims to classify the movement type of collapsed material from deep-seated landslides and to clarify the topographic conditions that influence it. The research area is the Kii Peninsula, south-western Japan, which was severely damaged by sediment-related disasters triggered by Typhoon Talas in 2011. A digital elevation model and aerial photographs were used in ArcGIS analysis, and topographic characteristics were examined to find significant factors that influenced the movement of collapsed material. Collapsed material of deep-seated landslides formed two main outcomes, debris flows and landslide dams. Debris flows were likely in catchments of streams with gradient > 10°and inflow angle <60°. Landslide dams were likely in catchments of streams with gradient < 10°and inflow angle > 60°. Landslide dams with an upstream watershed exceeding 100 km2 tended to remain for much shorter periods than those with smaller watershed. The equivalent coefficient of friction, representing the travel distance and degree of fluidization of collapsed material, could be used for predicting the deposition zone of collapsed material.
In the present study, we aim to assess landslide susceptibility and optimize causative factors using artificial neural network method in Ambon, Indonesia. Elevation, slope angle, slope aspect, lithology, geological density, proximity to river, proximity to faults, and proximity to road networks were chosen as the causative factors. Based on the obtained results, proximity to river and slope aspect were the least influential causative factors in the study, these two causative factors were then eliminated for the optimized landslide model. Proximity to road and geological density were proved to be the most influential causative factors. The six causative factors landslide susceptibility model returned better accuracy when compared to the eight causative factors landslide susceptibility model. The output susceptibility maps were reclassified into five classes ranging from very low to very high susceptibility using Jenks natural break method. 20% of all mapped landslides were used as the validation of the susceptibility models. Receiver operating curves (ROCs) were calculated, the areas under the curve (AUC) for the success rate curves of six factors landslide susceptibility map and eight factors landslide susceptibility maps were 0.770 and 0.734, respectively. The AUC for the prediction rate curve for the six factors and eight factors landslide susceptibility maps were 0.777 and 0.717, respectively.