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研究生:張凱堯
研究生(外文):Kai-Yao Chang
論文名稱:人工智慧於都市防洪排水系統控制之研究
論文名稱(外文):Artificial Intelligence for City Flood Control System
指導教授:張斐章張斐章引用關係
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:生物環境系統工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:119
中文關鍵詞:水位預測抽水站操作倒傳遞類經網路(BPNN)反傳遞類經網路(CFNN)調適性模糊類神經網路(ANFIS)
外文關鍵詞:Water-level predictionPumping operationBack-propagation neural networks(BPNN)Counterpropatagation fuzzy neural network(CFNN)Adaptive network-based fuzzy inference system(ANFIS)
相關次數:
  • 被引用被引用:4
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  • 收藏至我的研究室書目清單書目收藏:1
雨水下水道系統為都市安全輸送暴雨逕流,減輕都市淹水風險的重要設施,然而都市化造成的高度開發,使降雨集水時間縮短,急遽暴雨在短時間內產生大量地表逕流無法及時排除,導致局部地區發生積水情形;而市區排水系統配合都市開發而逐漸地下化,使下水道系統水理狀況不易掌握,更增加都市防洪排水管理的困難度。本研究目的在透過人工智慧相關技術,模擬人類學習、適應、回想等能力,以解決都市排水複雜的非線性與時變性系統問題,建構市區雨水下水道水位預測模式及防洪抽水站即時操作指引模式,提供管理者進行都市防洪排水整體操控策略規劃之參考。本研究首先探討倒傳遞類神經網路(BPNN)於雨水下水道水位預測模式之應用,經測試不同時距輸入資料格式之多階段水位預測模式,成功建立預測準確性極高之雨水下水道水位預測模式,並探討得知短時距輸入資料格式建立之水位預測模式比長時距資料格式更能掌握未來時刻雨水下水道水位變化,此水位預測模式除為都市積水預警提供更寬裕的應變時間外,亦提供防洪抽水站操作對掌握未來時刻水位變化之需求。其次探討反傳遞模糊類神經網路(CFNN)及調適性模糊類神經網路(ANFIS)於都市防洪抽水站即時操作指引模式之應用,研究結果顯示,透過降雨量、前池水位變化、閘門啟閉及抽水機組操作等輸入資料,CFNN及ANIFS即能建置準確性極高的抽水操作指引模式;而ANFIS因具備高度學習能力,利用少數規則即可預測較CFNN更精確的未來時刻抽水機組操作需求,因此應用ANFIS建構之防洪抽水站即時操作指引模式,將可提供抽水站管理人員即時操作建議,提昇抽水站操作效率及安全性。
Drainage systems play an important role in transporting storm runoff and reducing flood risk in urban areas. Stormy water, discharged by underground drainage systems, is hard to control, especially in highly urbanized areas, where concentration time is shorten and runoff coefficients are increased. This study aims to construct water-level prediction models in urban drainage systems and real-time operational guidelines for flood control pumping stations by using artificial intelligent techniques (AI). The AI techniques could effectively solve highly non-linear control problems and robustly tune the complicated conversion of human intelligence to logical operating system.
This study first applies back-propagation neural networks (BPNN) to predict water-level in the urban drainage systems of Taipei city. The results show that BPNN could satisfyingly predict the water level with high accuracy. The model provides much longer responding time for urban flood management. The application also indicates that input data with shorter time interval has higher accuracy, which meets the need of pumping operation.
The real-time operation guidelines for pumping stations in urban areas are future investigated by using counterpropagation fuzzy-neural network (CFNN) and adaptive network-based fuzzy inference system (ANFIS). The results demonstrate that CFNN and ANFIS are both capable of forming reliable guidelines by using the information of precipitations, fore-bay water levels, gate operation and number of pumping station. It also indicates that ANFIS, comparing to CFNN, has better learning algorithm, which requires less rules to meet accuracy pumping operation needs. The real-time operation guidelines formed by ANFIS are recommended to managers for promoting operation efficiency and reliability.
摘 要 I
Abstract II
目 錄 III
圖目錄 VI
表目錄 IX
第一章 緒論 1
1-1研究緣起 1
1-2 都市排水特性 2
1-3研究目的 4
1-4 研究架構 6
第二章 文獻回顧 10
2-1 都市防洪排水系統研究 10
2-1.1都市雨水下水道系統 10
2-1.2防洪抽水站操作 12
2-2模糊理論 13
2-3模糊推論系統 14
2-4類神經網路 15
2-4.1倒傳遞類神經網路 16
2-4.2調適性網路模糊推論系統 17
2-4.3反傳遞模糊類神經網路 18
第三章 人工智慧理論概述 19
3-1 模糊理論 20
3-1.1模糊推論系統 25
3-2 類神經網路 27
3-2.1倒傳遞類神經網路 31
3-2.3反傳遞模糊類神經網路 41
3-2.3調適性網路模糊推論系統 46
第四章 智慧型都市雨水下水道水位預測模式 55
4-1 研究區域概述 56
4-1.1 抽水站現況說明 56
4-1.2 水文資料蒐集與分析 59
4-2 網路架構選取 61
4-3 評估指標 63
4-4 CGBP雨水下水道水位預測模式 65
4-4.1水位預測模式 65
4-4.2 5分鐘預測結果 67
4-4.3 10分鐘預測結果 69
4-4.4 20分鐘預測結果 73
4-4.5 30分鐘預測結果 77
第五章 智慧型防洪抽水站操作指引模式 82
5-1抽水站現行操作評析 83
5-1.1 抽水站操作說明 83
5-1.2 影響操作因子評析 86
5-2 CFNN防洪抽水站操作指引模式 87
5-2.1 網路架構 87
5-2.2 參數設定及模式評析 89
5-3 ANFIS防洪抽水站操作指引模式 95
5-3.1 網路架構及參數設定 95
5-3.2模式評析 102
第六章 結論與建議 105
6-1 結論 106
6-1.1雨水下水道水位預測模式 106
6-1.2防洪抽水站即時操作指引模式 107
6-2建議 108
參考文獻 110
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1. 91.王安培、鄭博文,1998,“模糊決策在水庫操作之應用”,中原學報,26(3):17-25。
2. 103.孫志鴻、詹仕堅,1999,“類神經網路在集水區降雨逕流模擬之應用”,國立臺灣大學理學院地理學系地理學報,25:1-14。
3. 105.張凱堯、張斐章,2007,“反傳遞模糊類神經網路於抽水站操作之應用”,農業工程學報,53(1):82-91。
4. 106.張斐章、徐國麟,1990,“利用模糊集理論推估河川流量之研究”,中國農業工程學報,第36卷第4期,pp. 1-12。
5. 107.張斐章、黃源義、梁晉銘,1993,“模糊推論模式之建立及其應用於水文系統之研究”,中國農業工程學報,39(1):71-83。
6. 108.張斐章、孫建平,1998,“類神經網路及其應用於降雨-逕流過程之研究”,中國農業工程學報,44(1):34-49。
7. 110.張斐章、胡湘帆、蕭錫清、張長圖,2000,“模糊類神經網路於水庫即時入流量預測之應用”,台電工程月刊,618:7-19。
8. 112.張斐章、陳彥璋、梁晉銘,“以類神經網路預測淡水河感潮河段水位”,農業工程學報,第47卷,第4期,第29-38頁,民國90年12月。
9. 113.張斐章、黃源義、梁晉銘,1993,“模糊推論模式之建立及其應用於水文系統之研究”,中國農業工程學報,第39卷,第1期,pp.71-83。
10. 115.張斐章、惠士奇,1998,“灰色模糊序率動態規劃於水庫操作之應用”,中國農業工程學報,44(1): 34-49。
11. 116.張斐章、胡湘帆、黃源義,1998,“反傳遞模糊類神經網路於流量推估之應用”,中國農業工程學報,第44卷,第2期,pp.26-38。
12. 121.張麗秋、張斐章,1999,“智慧型水庫即時操作控制系統”,中國農業工程學報,第45卷,第4期,pp.18-30。
13. 125.陳昶憲,1992,“水庫防洪即時優選操作模式目標函數與限制式之探討”,水利工程,36:90-96。
14. 126.陳昶憲、楊朝仲、王益文,1996,“類神經網路於烏溪流域洪流預報之應用”,中華水土保持學報,27(4):267-274。
15. 127.陳昶憲、陳建宏,1999,“類神經模糊邏輯法應用於洪水位預報”,中國土木水利工程學刊,第11卷,第2期,pp. 317-32。