Research On Simulation Of Urban Waterlogging Based On GIS And SWMM Model

In this paper, we used SWMM model and GIS technology in combination to study the regional rainstorm waterlogging mechanism, finally we established the rainstorm waterlogging model of the study area. then we calibrated the parameters of the model and proposed an algorithm to diffusion the water of catchment, we carried on the simulation experiment with the rainfall data on June 18,2016. The error between the simulation result and the measured one showed the model we established a well performance.The major studies of this paper are as follows:(1) We established the SWMM model of the study area based on GIS, Firstly we proposed the coupling framework of SWMM model and GIS technology. We used the spatial analysis function of GIS to acquire the parameters of the model and improved the access method of catchment by establishing the theoretical formula to revise the DEM, then we acquired the refined catchments based on the revised DEM and D8 algorithm ,finally we established the SWMM model of the study area based on the parameters acquired(2)We proposed a new calibrating method which combines the random and certainty algorithm. In this way, we can not only avoid the low efficiency problem, but also solve the issues of weak local search ability and trapping into the optimal solution.Verification by the rain data of 20160618 and 20160914 showed the calibrating algorithm a well performance.(3)We proposed a dynamic diffusion algorithm based on rainfall pattern. After calculating the period spray water according to the established theoretical formula,we established the diffusion algorithm based on the water volume balance principle and test method. We can get the depth of the waterlogging area and the distribution of the waterlogging. In order to display the simulation result, we established the standard of depth and color.(4)We applied the model we established to the study area and verified the effectiveness of it. We simulated the flood situation with the rainfall data of June 18,2016?September 14,2016. The comparison between the simulated result and the measured data showed the effectiveness of the model.