Simultaneous Optimization of SWMM Parameters by the Dynamic System Response Curve with Multi-Objective Function

Yao Du, Qiongfang Li (), Pengfei He, Zhenhua Zou, Zhengmo Zhou, Shuhong Xu, Xingye Han and Tianshan Zeng
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Yao Du: Hohai University
Qiongfang Li: Hohai University
Pengfei He: Yellow River Institute of Hydraulic Research
Zhenhua Zou: Middle Changjiang River Bureau of Hydrological and Water Resources Survey, Changjiang Water Resources Commission
Zhengmo Zhou: Hohai University
Shuhong Xu: Hohai University
Xingye Han: Hohai University
Tianshan Zeng: Hohai University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 13, No 6, 5079 pages

Abstract: Abstract The effective and efficient optimization of Storm Water Management Model (SWMM) parameters is critical to improving the accuracy of the urban rainfall-runoff simulation. Therefore, it is necessary to investigate the applicability of the dynamic system response curve (DSRC) method in optimizing SWMM model parameters, which is newly proposed to solve the nonlinear problems encountered by current widely used optimization methods. A synthetic case, free of data and model errors, was used to examine the applicability of the DSRC with single-objective or multi-objective functions in finding the optimum parameter values known by assumption. A real watershed case was selected for the optimization of SWMM parameters by use of DSRC with the most suitable objective function, which was determined by a synthetic case. In addition, the advantages of the DSRC in SWMM parameter optimization over the Particle Swarm Optimization(PSO) and Multiple Objective Particle Swarm Optimization(MOPSO) algorithms were analyzed in terms of NSE, $${RE}_{v}$$ RE v , $${RE}_{p}$$ RE p , and $${EP}_{t}$$ EP t . The results revealed that the DSRC with multi-objective function could find the global optima of all SWMM model parameters in the synthetic case, but it could only attain part of them with a single-objective function. In the real watershed case, the DSRCS-optimized SWMM performed better than MOPSO-optimized one with an increase of average $$\mathrm{NSE}$$ NSE by 5.8% and a reduction of average $$\left|{RE}_{v}\right|$$ RE v , $$\left|{RE}_{p}\right|$$ RE p and $$\left|{EP}_{t}\right|$$ EP t by -53.7%, -67.9%, and -34.6% respectively during the study period. The outputs of this paper could provide a promising approach for the optimization of SWMM parameters and the improvement of urban flooding simulation accuracy, and a scientific support for urban flood risk control and mitigation.

Keywords: Dynamic system response curve; PSO/MOPSO algorithms; SWMM model; Parameter optimization; Multi-objective function (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03595-w

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