Fengchang Xue

For the challenge of parameter calibration in the process of SWMM (storm water management model) model application, we use particle Swarm Optimization (PSO) and Sequence Quadratic Programming (SQP) in combination to calibrate the parameters and get the optimal parameter combination in this research. Then, we compare and analyze the simulation result with the other two respectively using initial parameters and parameters obtained by PSO algorithm calibration alone. The result shows that the calibration result of PSO-SQP combined algorithm has the highest accuracy and shows highly consistent with the actual situation, which provides a scientific and effective new idea for parameter calibration of SWMM model, moreover, has practical guidance for flood control and disaster mitigation.

Assessment of urban water logging risk depth is mainly based on extreme value of rainstorm and its occurrence frequency as disaster causing factor. Regional waterlogging disaster risk assessment can be determined through regional geographic spatial information coupling calculation; the fundamental reason lies in the lack of an effective method for numerical simulation of waterlogging risk depth. Based on the hydrodynamic principle, FloodArea model realizes the numerical simulation of regional waterlogging depth by hydrologic calculating of runoff generation and runoff concentration of waterlogging. Taking risk assessment in Nanchang city as an example, spatial distribution of urban waterlogging depth was simulated by using FloodArea model in return period of 5 years, 10 years, 50 years, and 100 years. Research results show that FloodArea model can simulate urban waterlogging forming process and spatial distribution qualitatively.