A case study of debris flow risk assessment and hazard range prediction based on a neural network algorithm and finite volume shallow water flow model

Due to the need of economic development and energy structure adjustment, China intends to build a number of pumped storage power stations for hydroelectric storage to generate electricity. Pumped storage power stations are generally built in mountainous or hilly areas where sufficient water and height differences will provide the adequate head difference. Geological processes, such as debris flows, often occur in mountainous areas and are one of the main threats to power stations and related projects. In this work, the debris flows in the engineering area of a pumped storage power station in Shangyi County, Hebei Province were selected as a case study. The neural network model was adopted to quantitatively calculate the probability of debris flows. Then, risk zoning was implemented according to the probability values. Finally, the debris flow numerical simulation software SFLOW, which is based on the finite volume shallow water flow model, was used for high-risk gullies. The spatial hazard range of each debris flow was predicted for rainfall frequencies of 20, 50, 100, and 200 years. And the sensitivity of parameters affecting debris flow migration and the advantages of SFLOW compared with FLO-2D software were discussed. In general, the SFLOW model can accurately and efficiently solve the problem of fluid flow on irregular terrain and can be applied to similar engineering projects.