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\u27 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

A Multi-Objective Optimal Allocation of Treated Wastewater in Urban Areas Using Leader-Follower Game

This study proposes a new method for optimal allocation of Treated Wastewater (TW), in which different stakeholders, their social position in decision-making, and priority of objectives were attended using the leader-follower game theory. The suggested methodology was applied in a case study in the eastern part of Tehran province in Iran, where the Water and Sewage Department is considered the leader and four TW dependent districts are the followers in the game model. The leader appropriates a certain TW quantity to the system, and the followers compete for the allocated resources in the face of various physical and sociopolitical constraints. The Nash-Harsanyi production function was applied to model the non-cooperative relationships among the followers (i.e., their competition for limited resources) and find a compromise solution. Considering different limitations, such as the location and quantity of the TW allocation, 1569 different allocation scenarios were considered in four districts. Then, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based multi-objective optimization model was developed to optimize the main objectives of the leader, including i) minimizing the TW transfer costs, and ii) minimizing the supplied demand deficiency in all districts. A multi-criteria decision-making model was utilized to find the best solution on the achieved Pareto-front space. Nine different weighting scenarios were adopted to assess the model sensitivity to the importance of the selected criteria. Results point to the sensitivity of the framework to weighting scenarios, but provide effective compromise solutions to a complex system that can only partially supply water demands