Optimal Water Quality Management Strategies for Urban Watersheds Using Macrolevel Simulation and Optimization Models
Journal of water resources planning and management
OF DISSERTATION OPTIMAL WATER QUALITY MANAGEMENT STRATEGIES FOR URBAN WATERSHEDS USING MACRO-LEVEL SIMULATION MODELS LINKED WITH EVOLUTIONARY ALGORITHMS Urban watershed management poses a very challenging problem due to the various sources of pollution and there is a need to develop optimal management models that can facilitate the process of identifying optimal water quality management strategies. A screening level, comprehensive, and integrated computational methodology is developed for the
... nagement of point and non-point sources of pollution in urban watersheds. The methodology is based on linking macro-level water quality simulation models with efficient nonlinear constrained optimization methods for urban watershed management. The use of macro-level simulation models in lieu of the traditional and complex deductive simulation models is investigated in the optimal management framework for urban watersheds. Two different types of macro-level simulation models are investigated for application to watershed pollution problems namely explicit inductive models and simplified deductive models. Three different types of inductive modeling techniques are used to develop macro-level simulation models ranging from simple regression methods to more complex and nonlinear methods such as artificial neural networks and genetic functions. A new genetic algorithm (GA) based technique of inductive model construction called Fixed Functional Set Genetic Algorithm (FFSGA) is developed and used in the development of macro-level simulation models. A novel simplified deductive model approach is developed for modeling the response of dissolved oxygen in urban streams impaired by point and non-point sources of pollution. The utility of this inverse loading model in an optimal management framework for urban watersheds is investigated. In the context of the optimization methods, the research investigated the use of parallel methods of optimization for use in the optimal management formulation. These included an evolutionary computing method called genetic optimization and a modified version of the direct search method of optimization called the Shuffled Box Complex method of constrained optimization. The resulting optimal management model obtained by linking macro-level simulation models with efficient optimization models is capable of identifying optimal management strategies for an urban watershed to satisfy water quality and economic related objectives. Finally, the optimal management model is applied to a real world urban watershed to evaluate management strategies for water quality management leading to the selection of near-optimal strategies.