| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 94 === In recent decades, due to increasing on water demand by coupled with unbalanced temporal and spatial distribution of rainfall in Taiwan, water supply becomes more difficult. To overcome the problem, attention has been focused on improving water resources management and reservoir operation. Nevertheless, such actions frequently result on the degradation of river environmental condition. In an attempt to minimizing these negative impacts, environmental related aspects must be considered on the operation and management of water storage facilities, such as reservoirs.
In this study, we propose a novel optiming technique for reservoir operation to deal with multiple water users, specially emphasizing downstream ecological flow demand. The artificial intelligence techniques, such as genetic algorithm (GA), have been successfully applied for the optimization of complex hydrosystems. The GA is applied for the optimization of water resources management. To improve the capability of the GA, a penalty function is proposed, resulting in a restricted GA model. This penalty function is cooperatively considered with the reservoir operational objectives, which also includes water quantity and ecological aspects. To investigate the applicability of the proposed methodology, the downstream area of Shihmen reservoir from Hochih Weir to Yuanshan Weir in the Dahan River is used as the case study.
Based on several previous studies, the water supply of Shihmen reservoir for dowmstream could reach a base ecological flow to about 4 cms in most of time. By implemented the proposed restricted GA for optimizing the Shihmen reservoir operation in last 20 years, the results demonstrated model can provide much better the performances, in term of small GSI and large ecological flow, for most of years than the historical approach. We conclude that the restricted GA approach can improve the efficiency and effectiveness of reservoir optimization operation for mutilpe water users.
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