Parameter Calibration Of SWMM Model Based On Hybrid Particle Swarm Optimization

The problem of city water logging is getting worse because of the urbanization.It is a great significance for urban rainwater’s management and water logging’s solutions by using urban rain and flood model to simulate and analyze the situation of the city water logging.Sensitivity analysis and optimization rate of model parameters have been paid more attention in recent years.It is still a problem that the selection and verification of parameters lead to under-fitting of results.In this paper,urban storm flood model will be established based on SWMM by using the platform of geographic information system(GIS).And use the completed SWMM model to simulate water logging after adding LID combination measures in the study area and analyze the reduction effect of low-impact development on total runoff and peak runoff.The specific research results are as follows:1.Build the SWMM model.Using the spatial analysis technology of GIS to read and intelligently input the topographic information and CAD format data of the research area,import many data into the SWMM model,input rainfall data,and complete the model construction.2.Sensitivity analysis to SWMM model parameters.Finally,eight items are selected after the combined application of the modified Morris screening method and the global Sobol method,the parameter sensitivity index and ranking under the two output variables of total runoff flow and peak flow are obtained,and finally six parameter optimization sets are determined.3.Optimizing SWMM model parameters by Multi-objective hybrid particle swarm optimization(based on simulated annealing algorithm).The BP neural network toolbox for sample training was used to build the relationship between input variables and output variables.Taking the total production and peak flow as the optimization goals,the multi-objective basic particle swarm algorithm and the multi-objective hybrid particle swarm algorithm(based on the simulated annealing algorithm)are used for parameter calibration optimization.And the two parameters of water level and flow are used to verify the parameter results.It can be seen from the comparative analysis that the verification results meet the requirements after the simulation of optimized Rain1 and verified Rain2,the absolute percentage error of peak flow is all less than 5%,and the total runoff error is less than 7%.Compared with single-objective optimization,the parameter set obtained by multi-objective optimization is better.In summary,the best is the parameter combination optimized by the multi-objective hybrid particle swarm algorithm.4.Analysis on the effect of LID combination measures.According to land use type and other information,it has got five combinations including rain garden,rain tank,sunken green space,permeable pavement and green roof,suitable for the study area are added to the LID module of the model,and any four measures are combined.Analysis on the effect of LID combination measures for the total amount of runoff from the drainage outlet and the reduction of the peak runoff during the selected return period shows that the combined measures of option four are finally determined as the optimal measure according to the reduction effect and standards in the local design guidelines to increase the peak runoff control target to once in 50 years.