Balancing Losses of Multipurpose Reservoirs by an Integrated Knowledge-Based System

The present study proposes a knowledge-based system for optimizing the operation of multipurpose reservoirs in which the machine learning models, fuzzy inference systems, and particle swarm optimization are linked. The purposes were defined based on the key responsibilities of a reservoir and environmental requirements including (1) maximizing water supply, (2) maximizing hydropower production, (3) minimizing flood damage, and (4) mitigating the downstream environmental impacts, consisting of water quality and water quantity impacts on the aquatic habitats. The total loss was assessed by a fuzzy inference system in which flood damage, environmental loss, water supply loss, and hydropower production loss were the inputs of the system. Moreover, the fuzzy inference system was applied to assess the environmental loss of the reservoir. Two machine learning models were utilized to simulate water temperature and dissolved oxygen concentration. Moreover, the results of the knowledge-based system were compared with a conventional multiobjective optimization of reservoir operation in the case study. According to the results, the developed simulation-optimization method is able to optimize the release from the reservoir based on the defined purposes. However, it is not able to maximize benefits and mitigate environmental impacts perfectly. The proposed method can reduce the possible flood damage by more than 70%. The total loss is 60%, which corroborates that the performance of the proposed method is acceptable for balancing the conflicts of interest in the reservoir operation. Furthermore, results indicated that the knowledge-based system is able to reduce environmental impacts compared with the conventional multiobjective model in the case study.