| 研究生: |
王瑞鋐 Wang, Ruei-Hung |
|---|---|
| 論文名稱: |
考量下游水質及河川流態於水庫最佳化操作之研究 Optimal Reservoir Operation Considering Downstream Water Quality and Ecological Flow Regimes |
| 指導教授: |
孫建平
Suen, Jian-Ping |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 水利及海洋工程學系 Department of Hydraulic & Ocean Engineering |
| 論文出版年: | 2009 |
| 畢業學年度: | 97 |
| 語文別: | 中文 |
| 論文頁數: | 97 |
| 中文關鍵詞: | 河川流態 、倒傳遞類神經網路 、水質推估模式 、遺傳演算法 、水庫最佳化操作 、環境流態因子 |
| 外文關鍵詞: | Water quality model, Environmental flow components, Flow regime, Optimal reservoir operation, Artificial neural networks, Genetic algorithm |
| 相關次數: | 點閱:251 下載:7 |
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本研究以石門水庫作為研究區域,欲建立一個以旬為期距的水庫最佳化操作策略,在供應人類需水的同時,亦包含了對水質以及生態上的考量。台灣地區為了有效的利用水資源,以及於洪水來臨時防災等目的,許多河川都建造了水庫等水工結構物,導致下游河川中流量變化規則化,或是發電時短延時內大小落差嚴重的流量變化,皆造成河川中嚴重的生態影響,規則化的結果使得生態系統趨於單調,短延時內落差嚴重的流量變化則影響對於環境變化容忍度範圍較小的物種,使得生物多樣性降低,因此決策者於水資源管理時應注意流態變化如何影響河川生態系統。
最佳化模式中之目標函數由需水目標、水質目標、生態目標所結合,人類需水參考現行的各標的計畫需水量及M-5石門水庫運轉操作規則制定,生態方面則參考Richter等人(2007)所提出之環境流態因子(Environmental flow components,EFCs)作為生態考量目標。由於水庫放流的變化會影響下游水質的變化,因此先以倒傳遞類神經網路建構水質的推估模式,其中歷史流量、歷史雨量、歷史上游水質、量測水質之旬數為輸入,歷史下游水質為輸出,而水庫操作管理之流量代入推估模式得水質後,將其作為水質考量目標。確立需水目標、生態目標與水質目標後,結合此三大目標應用於建立水庫最佳化操作模式,以水庫旬蓄水歷線為最佳化模式中的決策變數,並應用遺傳演算法搜尋最佳化模式的最佳解,嘗試提出一個可供未來欲考量水質及生態之水資源管理政策上的參考。
This study develops an optimal reservoir management model based on each ten-day operation periods for Shihmen Reservoir. The model tries to not only meet the human water requirement, but also to consider water quality and ecological issues. In Taiwan, lots of infrastructures have been built in almost every river to effectively use water resources and provide disaster defense. It results in downstream flow regulation or violent flow fluctuation that causes impacts on aquatic ecosystem biodiversity. Decision makers should take flow variability into account in water resources management policy.
Human demands, water quality, and ecological issues are considered in the optimal reservoir management model. Human demands objective is based on water rights of different users and M-5 Operation Rules of Shihmen Reservoir. Ecological objective is based on consideration of Environmental flow components (Richter et al. 2007). Water quality model uses an artificial neural network model to simulate downstream water quality by using streamflow, rainfall, upstream water quality, and measure time as inputs. Then this model is combined with human demands and environmental flow components considerations to establish an optimal reservoir management model by using genetic algorithm. The optimal reservoir operation model considers human demands, water quality and flow regime to benefit to both human society and aquatic ecosystems.
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