A Game Theoretical Low Impact Development Optimization Model for Urban Storm Water Management
This study presents a novel framework to optimize Low Impact Development (LID) practices for urban storm water management. First, the Storm Water Management Model (SWMM) model was executed for different possible scenarios of input parameters and various LIDs to simulate runoff volume, Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) loads. Next, a neural network (MLP-ANN) was trained and validated, as a surrogate model, against the set of inputs and output variables from the SWMM model simulations. The inherent uncertainties in the rainfall-runoff modeling were accounted for using a Nonlinear Interval Number Programming (NINP) model, and stakeholders' interactions are considered using a leader-follower game model. Velenjak urban watershed in Tehran, Iran, with four key stakeholders was considered as the study area. Tehran Municipality is the financial service provider that makes the first decision the leader-follower structure, and is considered as the leader with the priority of minimizing LIDs’ construction and maintenance costs. Tehran Department of Environmental Protection, Tehran Regional Water company, and Tehran Province Water and Wastewater company are the main followers in the decision making structure in the study area. This game theoretical framework yields several Pareto optimal solutions given the conflicting utilities of various players, and a Multi Criteria Decision Making procedure – i.e. PROMETHEE model – selects the most preferred compromised option. Fourteen weighting scenarios were considered in the PROMETHEE model to determine the compromise solution among the 52 solutions on the trade-off curve. The novelty of this study lies in using a nonlinear interval conflict resolution multi-objective optimization model for urban storm water management based on a leader-follower game. The proposed methodology warrants BOD and TSS loads, as well as storm water volume, are all reduced, with the highest reductions of 93, 86 and 90 percent, respectively. Results testify to the efficacy of the proposed model for urban storm water management.
Latifi, Morvarid; Rakhshandehroo, Gholamreza; Nikoo, Mohammad Reza; and Sadegh, Mojtaba. (2019). "A Game Theoretical Low Impact Development Optimization Model for Urban Storm Water Management". Journal of Cleaner Production, 241, 118323. https://doi.org/10.1016/j.jclepro.2019.118323