Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks |
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| Authors: | Paulo Chaves Toshiharu Kojiri |
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| Institution: | Water Resources Research Center, Disater Prevention Research Institute (DPRI), Kyoto University, Gokasho, Uji, Kyoto 6110011, Japan |
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| Abstract: | By taking advantage of the close relationship between quality and quantity of water, we investigated the potential improvements of the in-reservoir water quality through the optimization of reservoir operational strategies. However, the few available techniques for optimization of reservoir operational strategies present some limitations, such as restrictions on the number of state/decision variables, the impossibility considering stochastic characteristics and difficulties for considering simulation/prediction models. One technique which presents great potential for overcoming some of these limitations is applied here and investigated for the first time in such complex system. The method, named stochastic fuzzy neural network (SFNN), can be defined as a fuzzy neural network (FNN) model stochastically trained by a genetic algorithm (GA) based model to yield a quasi optimal solution. The term “stochastically trained” refers to the introduction of a new loop within the training process which accounts for the stochastic variable of the system and its probabilities of occurrence. The SFNN was successfully applied to the optimization of the monthly operational strategies considering maximum water utilization and improvements on water quality simultaneous. Results showed the potential improvements on the water quality through means of hydraulic control. |
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| Keywords: | Stochastic optimization Reservoir operation Water quality Intelligent system Stochastic fuzzy neural network Evolving neural networks |
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