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A Framework for Optimization and Simulation of Reservoir Systems Using Advanced Optimization and Data Mining Tools


The simulation and optimization of reservoir systems has attracted a great deal of attention in the field of hydrology and water resources management. Although many advances have been made, the gap between theoretical and real-world operation of reservoir systems still exists. Here, multiple tools and algorithms are proposed to bridge the existing gap to some extent. These tools are developed to aid decision makers, engineers, and scientists to understand, simulate, and improve the operational rules of reservoir systems.

In this dissertation, I propose an optimization framework, titled shuffled complex-self adaptive hybrid evolution (SC-SAHEL), to optimize the controlled discharge from reservoirs. This new optimization tool can solve a wide range of optimization problems, using a self-adaptive search mechanism. The algorithm employs multiple search methods from different optimization tools and selects the most suitable method for the problem space. This process reveals the potential of each search mechanism during the course of the search and enhances the efficiency and effectiveness of the search. The SC-SAHEL framework is tested on multiple benchmark problems and showed superior performance in comparison to single search methods. The framework is applied to the Folsom reservoir to maximize hydropower generation by tuning the controlled discharge. The results showed that the SC-SAHEL algorithm is superior to other single search method algorithms for finding near optimum solutions. The results also demonstrated the robustness of the SC-SAHEL framework for solving a wide range of reservoir optimization problems.

In addition to the optimization framework, a new data-mining algorithm, title generalized model tree (GMT), is proposed for simulating rule-based hydrologic systems. The new framework is developed based on the decision tree models and employs linear regression for prediction and model induction. The newly developed framework is tested on several benchmark datasets to compare its performance with other popular decision tree models. The framework can generate simple models to replicate different rule-based hydrologic systems. The simple structure of the inducted models makes them easy to implement and use in any programming languages and modeling framework. The framework is employed to simulate controlled discharge from multiple reservoirs across the Contiguous United States (CONUS). The results revealed the potential of the GMT framework for generating reservoir routing models. The models inducted by the GMT framework can be employed as a reservoir module within the hydrologic models, especially in large scale hydrologic modelling. In addition, the GMT framework model structure can reveal useful information about the underlying structure of the system. Hence, it can reveal information about importance of decision variables in the real operation of the reservoir systems.

The proposed tools benefit stakeholders to understand and improve their management practices of reservoir systems. The simulation method reveals the hydrologic response of natural systems to historical reservoir operation through reservoir routing, while shed light on the importance of decision variable in real operation of the system. The optimization algorithm finds the optimum operational rules for the reservoir systems for the specified goal and objective. The outcome of these two algorithms can be used for evaluating reservoir management practices, while considering the hydrologic behavior of the watersheds.

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