Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City

Hong Men, Hao Lu, Wenjuan Jiang and Duo Xu

Mathematical Problems in Engineering, 2020, vol. 2020, 1-17

Abstract:

Aiming at the optimization layout of distributed low-impact development (LID) practices in the sponge city, a new mathematical method combining Stormwater Management Model (SWMM) and preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) was developed and was applied in the Ximen waterlogged area of Pingxiang City. Firstly, a block-scaled rainfall-runoff model was built in the study area by using SWMM. Then, an LIDs area optimization model was established by linking the SWMM and the PICEA-g based on the Matlab platform, which took the area ratios of various LIDs in each block as decision variables and took the total runoff, peak flow, suspended substance (SS) pollutant, and LIDs cost as objective functions. Thus, the problem of LIDs layout was turned into a mathematical optimization issue. So the cost-benefit optimal solutions with different emphases were found by using this algorithm, and the LIDs layout optimal scheme for this area was further analysed and verified by rainfall-runoff model. The results show that the total runoff reduction rates of the system reach a maximum of 21.8%, the peak flow reduction rates of the system are more than 10%, and the SS pollutant reduction rates are reduced by about 30% compared with before LIDs under the design storms of different return periods. The reduction rates of each runoff index are higher than the nondominated sorting genetic algorithm II (NSGA-II) method, and decision-makers can more effectively analyse the cost-benefit optimal solution from the Pareto solution sets. Therefore, the LIDs layout optimization method proposed in this paper has obvious advantages in solving similar many-objective optimization problems (MOOPs) in sponge city construction.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6734081

DOI: 10.1155/2020/6734081

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