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.
Highlights
In the past 40 years, China’s urbanization rate has increased by nearly 40% under the global trend of rapid urbanization and modernization [1]. e high degree of urbanization has changed many ecological green spaces into impermeable pavements, resulting in a weakening of urban natural ability to absorb rainwater
One of the effective methods to solve the above problems is the low-impact development proposed by the US Environmental Protection Agency (EPA), which focuses on the reasonable layout of distributed small-scale green infrastructure, collects and processes rainwater from the source to achieve the purpose of controlling storm runoff [6, 7]
Cost-Benefit Optimal Solution. e significance of multiobjective optimization is to trade off multiple objectives, so as to obtain a nondominated solution that makes all objective function values as optimal as possible, which is generally obtained by analyzing the Pareto optimal front
Summary
In the past 40 years, China’s urbanization rate has increased by nearly 40% under the global trend of rapid urbanization and modernization [1]. e high degree of urbanization has changed many ecological green spaces into impermeable pavements, resulting in a weakening of urban natural ability to absorb rainwater. Researchers use storm management model tools, such as SWMM, InfoWorks CS, and MIKE URBAN, to simulate the Mathematical Problems in Engineering effect of runoff control in a certain area of the city after applying some LIDs or LID combination practices and formulate rainwater system planning solutions [10,11,12]. Paola et al [19] propose a decision Support System (DSS) based on harmony search algorithm to reduce both the flooded volumes and the expense of LIDs. Xu et al [20] couple nondominated sorting genetic algorithm II (NSGAII) to SWMM through Python and comprehensively consider the runoff index, pollutant load, and construction cost index to select a cost-effective solution as the final site-scale LIDs layout planning. Considering the above analysis, we establish an optimization model of LIDs layout by using the Matlab platform to combine SWMM with a many-objective optimization algorithm. e optimization model aims to reduce the total runoff, peak flow, SS pollution, and the cost of LIDs, and the PICEA-g algorithm is used to solve the optimal LIDs area ratio in each block
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