2022 Volume 3 Issue J2 Pages 339-345
In recent years, sediment disasters caused by heavy rains and typhoons have occurred frequently in Japan. Especially within the Fuji River Basin in central Japan, many regions are vulnerable to sediment disasters. For the risk reduction of sediment disasters, it is necessary to develop a sediment disaster risk estimation method that considers both trigger and inherent factors.
This study developed and validated a method to estimate sediment disaster risk that directly considers trigger and inherent factors in the areas around the Fuji River Basin (Yamanashi and Shizuoka prefectures) using a clustering and deep learning method. A fully connected deep neural network was used as the deep learning method. As the input data for the trigger factor, 60 minutes accumulated rainfall and soil water index within each cell were used. The horizontal resolution of cell size was around 1km. As the input data for inherent factors, the maximum slope angle and presence of faults within each cell were used. The sediment disasters caused by typhoons on 6th September 2007, 21st September 2011, and 12th October 2019 were used for training, determination of threshold, cross-validation, and validation of the deep learning method. For quantitative validation, we estimated the sediment disaster risk from neural network outputs and validated it by comparing sediment disaster occurrence reports. In the validation, an administrative area is regarded as "True Positive" if there are any" high-risk cells" and any "confirmed disaster cells" in the same area. We calculated the evaluation metrics based on a confusion matrix for validation.
Our method shows accuracy (0.314), indicating that the estimated sediment disaster risk is adequate. Therefore the risk information can help the decision-making of evacuation in the future. However, the False Alarm Rate was still high (0.904), so further improvements are required in future studies.
