指導教授：張斐章臺灣大學：生物環境系統工程學研究所呂英睿Lu, Ying-RayYing-RayLu2014-11-262018-06-292014-11-262018-06-292014http://ntur.lib.ntu.edu.tw//handle/246246/261745　　高度開發城市因面臨都市化與氣候變遷導致洪峰流量快速上升之影響，使得都市防洪成為其重要議題。因此，為減輕未來洪水帶來損失，建立一有效率且準確之模式以預測洪汛時期市區抽水站之內外水位與指引適當抽水機操作有其迫切性與重要性。近年來，人工智慧技術運用於處理高度複雜之非線性系統有出色的處理能力，本研究運用該技術於即時水位預報等模式上。本研究選定臺北市玉成抽水站為研究對象，研究目標為建置即時水位預報和抽水機組操作策略兩種模式。 　　本研究首先蒐集區域內水文計量站之歷史紀錄，通過相關性及歷程分析搜尋降雨逕流之稽延關係，並運用Gamma Test由多種雨量資訊篩選影響水位之重要因子，進而決定模式輸入因子。模式建置部分，使用一種靜態類神經網路(倒傳遞類神經網路─BPNN)，二種動態類神經網路(隱藏層回饋式類神經網路─Elman NN, 非線性自回歸與外部輸入類神經網路─NARX)建立多時刻內水位預報模式；外水位預報模式則以三階自迴歸模型(AR3)建立，並比較評估水位預報模式之準確度與穩定性。本研究第二部分將著重於抽水站抽水機組操作策略，蒐集其操作之歷史紀錄，包含抽水機組、重力閘門操作紀錄，並配合水位預報模式之輸出水位值進行相關性及歷程分析，探討影響抽水機組操作之機制。最後，調適性網路模糊推論系統(ANFIS)負責整合水位預報模型建立多時刻抽水機組操作策略模式。 　　結果顯示，動態類神經網路NARX訓練之內水位預報模式表現優於另外兩種網路；AR3模式能於1小時內準確推估外水位；另ANFIS所整合之抽水機組操作模式能精確且穩定模擬歷史人為操作歷程。本研究所提出之模式與方案，在洪汛時期可提供各抽水站中操作人員與決策者適當且即時之抽水機組操作策略，進而減輕洪災以達成都市防洪之目的。　　Urban flood control is a crucial task in developed cities, which faces a great challenge of fast rising peak flows resulting from urbanization and climate change. Therefore, it is imperative to construct an efficient and accurate model to forecast the water levels of rivers in urban areas during flood periods. Artificial intelligence (AI) techniques possess an outstanding ability to handle highly non-linear complex systems and are implemented to make real-time water level forecasts in this study. The Yu-Cheng pumping station located in Taipei City, Taiwan, is selected as the study area. The purpose of this study is to construct water-level forecasting models and a real-time operating strategy of pumps. 　　The first part of the study is dedicated to water level forecasting. Hydrological data were collected and fully explored by statistical techniques to identify the time span of rainfall affecting the rise of the water level in the floodwater storage pond (FSP) at the pumping station. Effective factors (rainfall stations) that significantly affect the FSP water level are extracted by the Gamma test (GT). For model construction, one static artificial neural network (ANN) (backpropagation neural network-BPNN) and two dynamic ANNs (Elman neural network - Elman NN; nonlinear autoregressive network with exogenous inputs - NARX network) are used to construct multi-step-ahead FSP water level forecasting models. A third-order autoregressive model (AR3) is used to construct multi-step-ahead river water level forecasting models. 　　The second part of this study is dedicated to operating strategy. Historical operating records of pumps at the pumping station were collected. Effective factors that affect the pumping operation are explored through the correlation analysis between the forecasted FSP and river water levels and historical hydrographs and operating records. The adaptive network-based fuzzy inference system (ANFIS) is used to construct multi-step-ahead operation strategy models in consideration of water level forecasting models. 　　The results demonstrate that the dynamic NARX network is superior to the two other comparative ANN models in making FSP water level forecasting; the AR3 model can accurately make river water level forecasting; and the ANFIS can suggest an operating strategy that suitably and reliably simulates historical pumping operations. The proposed methodology can provide managers and operators of pumping stations with a guideline for making suitable real-time pumping operation in response to drastic water level variations during flood periods, which is beneficial to urban flood control management.謝誌 I 摘要 IV Abstract VI 目錄 IX 圖目錄 XII 表目錄 XV 第一章 緒論 1 1.1 研究緣起 1 1.2 研究目的 3 第二章 文獻回顧 6 2.1 抽水站水位預測與操作規劃 6 2.1.1 國內研究論文 6 2.1.2 國外研究論文 8 2.2 類神經網路應用於抽水站系統 14 第三章 理論概述 15 3.1 相關性分析 15 3.2 還原內水位 17 3.2.1 額定抽水量推估法 17 3.2.2 機組性能曲線推估法 20 3.3 Gamma Test 22 3.4 類神經網路 24 3.4.1 倒傳遞類神經網路(BPNN) 26 3.4.2 隱藏層回饋式類神經網路(Elman NN) 31 3.4.3 非線性自回歸與外部輸入類神經網路(NARX) 33 3.4.4 調適性網路模糊推論系統(ANFIS) 35 3.5 評估指標 41 3.5.1 均方根誤差(RMSE) 41 3.5.2 相關係數(CC) 41 3.5.3 效率係數(CE) 42 3.5.4 尖峰水位誤差(EWLp) 42 3.5.5 尖峰水位到達時刻誤差(ETp) 43 3.5.6 平均絕對誤差(MAE) 43 第四章 研究區域之資料蒐集與概況分析 44 4.1 研究區域介紹 44 4.2 研究標的抽水站之規劃與操作現況 48 4.3 資料蒐集與前處理 53 4.3.1 還原內水位歷線方法評估 58 第五章 智慧型抽水站內外水位預測模式 61 5.1 相關性與歷程分析概論 61 5.1.1 水位與雨量相關性分析 61 5.1.2 水位與雨量歷程分析 68 5.1.3 水位自相關性分析 70 5.2 篩選影響因子概論 71 5.3 建置水位預報模式 73 5.3.1 抽水站之多時刻內水位預報模式 73 5.3.2 抽水站之多時刻外水位預報模式 83 第六章 智慧型抽水站操作策略整合模式 87 6.1 相關性與歷程分析概論 87 6.1.1 機組操作與重力閘門歷程分析 88 6.1.2 機組操作與水位相關性分析 91 6.1.3 機組操作與水位差相關性分析 93 6.1.4 機組操作紀錄自相關性分析 95 6.2 篩選影響因子概論 96 6.3 整合操作策略模式 97 6.3.1 抽水站之多時刻機組操作模式 97 第七章 結論與建議 114 7.1 抽水站水位預報 114 7.2 抽水站操作 115 7.3 研究建議 116 第八章 參考文獻 1184486135 bytesapplication/pdf論文公開時間：2015/08/21論文使用權限：同意有償授權(權利金給回饋本人)都市防洪防洪抽水站Gamma Test類神經網路自迴歸模型(AR)人工智慧(AI)水位預報抽水站操作策略[SDGs]SDG11[SDGs]SDG13thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/261745/1/ntu-103-R01622024-1.pdf