Abstract

One of the strategies to manage and reduce the problems caused by floods is to use different Low-Impact Development (LID) systems. Due to the existence of different types of LID systems, it is difficult to choose the best combination of them. The purpose of this research is to rank different LID systems and choose the best combination or arrangement of them under climate change using TODIM multi-criteria decision making method, which has not been done so far, in the northernmost of Tehran city (Iran), which also includes part of Tehran municipality districts 1 and 3 and it is adjacent to Tehran municipality district 6. Previously by the authors, surface runoff in the near future (2030–2047) and far future (2048–2065) under three scenarios of Representative Concentration Pathway (RCP2.6), (RCP4.5), and (RCP8.5) with the Storm Water Management Model (SWMM) was simulated (Dougaheh et al., 2023). Next the LID systems, including Vegetative Swale, bio-retention cell, infiltration trench and permeable pavement, are optimized with Multi-Objective Harris Hawks Optimization (MOHHO) algorithm. Then, using the Pareto Fronts obtained from the optimization algorithm, seven combinations of different LID systems were extracted as final alternatives. Then, the objective functions of the optimization algorithm, including the minimization of the volume of outflow runoff of the basin and the minimization of the cost of construction and maintenance of LID, are determined as problem criteria and weighted by the Entropy-Shannon method. The results show that the volume of urban runoff with the use of LID systems, in all future time periods under RCP2.6, RCP4.5, and RCP8.5 scenarios, will decrease by an average of 54% compared to the baseline time period, and the maximum percentage of volume reduction of the flood is related to the baseline period. In the future research, taking into account the qualitative parameters, the performance of the urban runoff collection system can be evaluated qualitatively in addition to quantitatively.

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