Automatic calibration of SWMM parameters based on multi-objective optimisation model

Abstract

To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application. HIGHLIGHTS The study provides a quantitative interpretation of the results of sensitivity parameter screening and ranking for the SWMM model.; During the construction of the objective function, the SWMM model system's multi-objective continuous optimisation criteria were adopted.; Combining the concepts of uncertainty and optimisation.;

Keywords

• glue
• multi-objective parameter calibration
• nsga-3
• sobol
• swmm