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研究生:楊琬茹
研究生(外文):Yang, Wan-Ju
論文名稱:智慧化動床推估模式之發展與應用-以濁水溪為例
論文名稱(外文):Development and Application of Intelligent Mobile-bed Estimation Model - A Case Study on Zhuoshui River
指導教授:葉克家葉克家引用關係吳祥禎
指導教授(外文):Yeh, Keh-ChiaWu, Shiang-Jen
口試委員:周乃昉賴進松吳祥禎葉克家
口試委員(外文):Chou, Nai-FangLai, Jihn-SungWu, Shiang-JenYeh, Keh-Chia
口試日期:2019-07-29
學位類別:碩士
校院名稱:國立交通大學
系所名稱:土木工程系所
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:2020
畢業學年度:108
語文別:中文
論文頁數:126
中文關鍵詞:CCHE1DAI類神經網路模式災害預警河川輸砂
外文關鍵詞:CCHE1Dearly warningartificial intelligence (AI)ANNfluvial sedimentation
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臺灣河川具坡陡流急之特性,集水區地質多屬砂、頁、板岩,質地脆弱,易崩塌,導致河流土砂生產量大,加上近年受到氣候變遷與極端降雨之影響,河道沖淤情形加劇,造成河道沖淤失衡、流路變遷。以往為了降低易致災河段發生災害的風險,電腦數值模式(如CCHE1D)被大量應用於河川輸砂之研究,提供防洪設施之河道穩定策略,如在危險河段中建置水工結構物,評估堤防高度是否足夠因應颱洪事件等。但在氣候變遷的衝擊下,降雨量往往出乎預料,預先進行的災害風險分析也無法確切描述災害程度,故即時評估流域水文及地文現況對河道沖淤變化之影響顯得格外重要。
本研究以濁水溪流域寶石橋至河口段為研究範圍,採用AI技術之類神經網路模式建置智慧化動床推估模式。因實測資料有限,故蒐集多變量蒙地卡羅法及隨機降雨序列機制衍生的多組事件帶入動床數值模式CCHE1D的模擬結果做為本模式資料來源。根據建置完成的ANN模式模擬結果可知,本研究發展的智慧化動床推估模式可短時間量化在不同水文、地文及輸砂因子的條件下,河道各斷面的最終底床沖淤狀況。未來應用方面可結合降雨預報系統,提前預警可能發生淤積過量或沖刷嚴重的河段,讓政府單位及沿岸居民做好完善的災前整備工作及災中應變作為。
Fluvial sedimentation and riverbed scour are common issues in Taiwan’s river because of steep terrain, fragile geology and torrents of rain that caused by typhoon. In general, in order to reduce the occurrence of fluvial sedimentation hazards, the numerical mobile-bed simulation model is widely applied in evaluating the riverbed change under various hydrological and geographical variables that provides governing agency to conduct river stability plan, such as reinforcement of spur dikes and groynes, etc. However, in recent years, Taiwan has influenced by climate change, and the rainfall intensity and magnitude of heavy rain has increased. It is uncertain to guarantee that the critical hydraulic structures could ensure the flood discharge capability under the changing climate. Accordingly, it is necessary to predict the real time riverbed change for early warning in order to reduce the impacts of extremely upcoming rainfall.
In this study, Baoshi Bridge to the Zhuoshui River estuary is chosen as the study area. The purpose is to establish an intelligent bed-elevation estimation (IBEE), which is simple and quick model to predict the riverbed change by AI technique (ANN). The data used in IBEE model, including multiple rainfall events and relevant factors which are intended to predict the riverbed change are generated by multivariates Monte Carlo simulation approach. In addition, due to the limited measurable riverbed elevation data, the corresponding riverbed changes are estimated through CCHE1D model, a 1D numerical mobile-bed simulation model, with the generated rainfall events. The result shows that IBEE model can quantify the riverbed change under consideration of factors, including rainfall factors (i.e., average rainfall depth and maximum rainfall intensity), physiographical factors (i.e., initial riverbed elevation and roughness coefficient) and sediment factors (i.e., coefficients of discharge-sediment rating curve and parameters of CCHE1D model). For further application, it could combine the rainfall forecast system to provide the early warning for reaching with serious deposition or scour, and the sophisticated pre-disaster preparation and disaster response.
目錄
摘要 I
Abstract II
誌謝 IV
目錄 V
表目錄 VIII
圖目錄 X
符號說明 XIII
第一章 緒論 1
1.1前言 1
1.2研究目的 1
1.3研究流程 2
1.4文獻回顧 3
1.4.1 人工智慧發展史 4
1.4.2 類神經網路發展史(ANN) 5
1.4.3 人工智慧於水利工程之相關研究 8
1.5本文架構 10
第二章 研究方法介紹 12
2.1 類神經網路演算法 12
2.2 CCHE1D 模式理論 17
2.2.1 水理模組 18
2.2.2 輸砂模組 19
2.2.3 輸砂參數特性說明 23
2.3 非常態多變量蒙地卡羅模擬(non-normal multivariate monte carlo simulation) 24
2.4 隨機降雨序列模擬機制(stochastic rainfall series generation model) 26
第三章 研究區域概述及ANN模式建置 29
3.1濁水溪流域概述 29
3.1.1 地理位置 29
3.1.2 地形與地質 30
3.1.3 水文氣象 30
3.2研究範圍 31
3.3 ANN資料庫建置 32
3.4 資料預處理 39
3.4.1 特徵選擇 39
3.4.2 資料標準化 42
3.5 ANN模型建置 42
3.5.1 訓練週期 44
3.5.2 初始權重 45
3.5.3 誤差函數 45
3.5.5 隱藏層與神經元 47
第四章 結果分析與討論 54
4.1 ANN模型效能評估 54
4.1.1河口至中沙大橋河段ANN預測分析 57
4.1.2中沙大橋至二水鐵路橋河段ANN預測分析 62
4.1.3二水鐵路橋至集集攔河堰河段ANN預測分析 67
4.1.4集集攔河堰至寶石橋河段ANN預測分析 72
4.2 歷史颱洪事件預測分析 77
4.3 應用範例說明 82
4.3.1 實際案例 82
4.3.2 設計案例 90
第五章 結論與建議 95
5.1 結論 95
5.2 建議 97
參考文獻 98
附錄一 ANN動床推估模式預測濁水溪河口至寶石橋河段之146個斷面的結果 103
附錄二 ANN動床推估模式預測濁水溪河口至寶石橋河段之146個斷面誤差結果 113
附錄三、各層隱藏層之間權重及偏權值 121
1. Armanini, A. and di Silvio, G. (1988). “A One-dimensional Model for the Transport of a Sediment Mixture in Non-Equilibrium Conditions.” Journal of Hydraulic Research, IAHR, 26(3), 275-292.
2. Bagnold, R.A. (1966). “An Approach to Sediment Transport Problem from General Physis.” Geological Survey Professional Paper.
3. Basser, H., Karami, H., Shamshirband, S., Jahangirzadeh, A., Shatirah, A., and Saboohi, H. (2014). “Predicting Optimum Parameters of a Protective Spur Dike Using Soft Computing Methodologies – A Comparative Study.”Computers & Fluids, 97, 168–176.
4. Bouzeria, H., Ghenim, A., and Khanchoul, K. (2017).“Using Artificial Neural Network (ANN) for Prediction of Sediment Loads, Application to the Mellah Catchment, Northeast Algeria.”Journal of Water and Land Development, 33,47-55.
5. Chang, C., Yang, J., and Tung, Y (1993). “Sensitivity and Uncertainty Analysis of a Sediment Transport Model: a Global Approach.” Stochastic Hydrology and Hydraulics, 7(4), 229-314.
6. Dawson, C.W. and Wilby, R. (1998). “An Artificial Neural Network Approach to Rainfall Runoff Modelling.” Hydrological Scienced Journal, 43(1), 47-66.
7. Dorofki, M., Elshafie, A.H., Jaafar, O., Karim, O.A., and Mastura, S. (2012). “Comparison of Artificial Neural Network Transfer Function Abilities to Aimulate Extreme Runoff Data.” Energy and Biotechnology, 33, 39-44.
8. Garbrecht, J., Kuhnle, R., and Alonson, C. (1995). “A Sediment Transport Capacity Formulation for Application to Large Channel Networks.” Journal of Soil and Water Conservation, 50(5): 527–529.
9. Heaton, J. (2008) “Introduction to Neural Network for Java” 2nd Edition, Heaton Research.
10. Hebb, D.O. (1949). “A Neuropsychological Theory.” A Study of A Science, 1, 622-643.
11. Hebb, D.O. (1949). “The Organization of Behavior -A Neuropsychological Theory.” McGill University.
12. Hopfield, J.J. (1982) “Neural Networks and Physical Systems with Emergent Collective Computational Abilities.” Proceedings of National Academy of Sciences, 79, 2554-2558.
13. HR Wallingford. (1990). “Sediment Transport, the Ackers and White Theory Revised.”, Report SR237.
14. Hsu, K.L., Gupta, H.V., and Sorooshian, S. (1995). “Artificial Neural Network Modeling of the Rainfall-Runoff Process.” Water Resources Research, 31(10), 2517-2530.
15. Hush, D. and Horne, B. (1993). “Progress in Supervised Neural Networks.” IEEE Signal Processing Magazine, 10, 8-39.
16. Javadi, F., Ahmadi, M.M., and Qaderi, K. (2015).“Estimation of River Bedform Dimension Using Artificial.” Sci. Tech, 17, 859-868.
17. Keshavarzi, A., Valizadeh, M., and Ball, J. (2010). “Experimental Study of the Effects of Submerged Dikes on the Energy and Momentum Coefficients in Compound Channel” Scientific Research, 2, 855-865
18. Kosko, B. (1988). “Bidirectional Associative Memories.” IEEE Transactions on Systms,Man, and Cybernetics, 18(1), 49-59.
19. Laursen, E.M. (1958). “The Total Sediment Load of Streams.” Journal of the Hydraulics Division, 84(1),1-36.
20. LC Van Rijn. (1984). “Sediment Pick‐Up Functions.” Journal of Hydraulic Engineering, 110(10).
21. Liu, P. and Der Kiureghian, A. (1986). “Multivariate Distribution Models with Prescribed Marginals Covariance.” Probabilistic Engineering Mech, 1(2), 105-112.
22. Maca, F., Pech, P., and Pavlasek, J. (2014). “Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast.” Mathematical Problems in Engineering, 1-10.
23. McCarthy, J., Minsky, M., Rochester, N., and Shannon, C.E. (1995). “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.”, 1-13.
24. Mcculloch, W.S. and Pitts, W. (1943). “ A Logical Calculus of the Ideas Immanent in Nervous Activity.” Bulletin of Mathematical Biophysis, 5, 115-133.
25. Meyer-Peter, E. and Muller, R. (1948). “Formulasfor Bed-Load Transport.” Hydraulic Engineering Reports, 39-65.
26. Minsky, M. and Papert, S.A. (1969). “Perceptrons: An Introduction to Computational Heometry.” MIT press.
27. Proffitt, G.T. and Sutherland A.J. (1982). “Transport of Non-Uniform Sediments.” Journal of Hydraulic Research, 21(1), 33-43.
28. Rosenblatt, F. (1957). “The Perceptron-A Perceiving and Recognizing Automaton.” Cornell Aeronautical Laboratory.
29. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). “Learning Representations by Back-propagation Errors.” Nature, 323(9).
30. Uca, Toriman, E., Jaafar, O., Maru, R., Arfan, A., and Ahmar, A.S. (2018). “Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network.” Journal of Physics, 954.
31. Veen, F.V. (2016). “The Neural Network Zoo.” The Asimov Institute.
32. Widrow, B. and Hoff, M.E. (1960). “Adaptive Switching Circuits.” IRE WESCON Convention Record, 96-104.
33. Wu, S.J., Yang, J., and Tung, Y.K. (2005). “Development and Application of Stochastic Generation Model.” National Chiao-Tung University.
34. Wu, W. and Vieira, D.A. (2002). “One-dimensional Channel Network Model CCHE1D.” National Center for Computational Hydroscience and Engineering, University of Mississippi, University, MS., 3.0. Technical Report No. NCCHE-TR2002-01.
35. Wu, W., Wang, and Jia, Y. (2000). “Nonuniform Sediment Transport in Alluvial Rivers.” Journal of Hydraulic Research, 38(6), 427- 434.
36. Yang, C.T. (1973). “Incipient Motion and Sediment Transport.” Journal of the Hydraulics Division, 99(10), 1679-1704.
37. Lhanchao (2016),「神經網路中前向傳播和反向傳播解析」,取自https://blog.csdn.net/lhanchao/article/details/51419150.
38. LYNN (2017),「早在三十年前,深度學習早就紅過了-淺談類神經網路曾經的瓶頸與衰頹」,取自https://kopu.chat/2017/10/27/nn-bottleneck/
39. 王天一(2018),「人工智慧革命:歷史、當下與未來」,如是文化。
40. 王如意、謝龍生、王鵬瑞(1998),「八掌溪流域降雨-逕流預報模式串聯應用之研究」,台灣水利,46(1)。
41. 行政院農業委員會水土保持局,土石流防災資訊網,fema.swcb.gov.tw/wap/
42. 余濬、洪志豪,「降雨量重現期推估之探討-以莫拉克颱風甲仙雨量站為例」,社團法人台北市水利技師公會。
43. 呂宜瑾(2019),「淺析台灣人工智慧醫療之發展」,取自https://portal.stpi.narl.org.tw/index/article/10499
44. 李彥宏(2017),「智能革命:迎接AI時代的社會、經濟與文化變革」,天下文化。
45. 林柏承(2000),「應用類神經網路於颱風降雨量的推估」,國立成功大學水利及海洋工程學系研究所碩士論文。
46. 段智懷(2004),「倒傳遞類神經網路小區域颱風降雨預報-前饋式與遞迴式之比較」,逢甲大學水利工程學系研究所碩士論文。
47. 國立交通大學防災工程研究中心(2017),「濁水溪本流河道長期穩定與經理對策研究(1/2)」,經濟部水利署第四河川局。
48. 張郁麟(2007),「倒傳遞類神經網路應用於臺灣北部水庫懸浮固體濃度即時分析與預測之研究」,國立臺灣大學生物資源暨隆學院生物環境系統工程學研究所碩士論文。
49. 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,44(2),26-38。
50. 陳宇文(1999),「類神經網路於入滲池最佳化設計之應用」,國立交通大學土木工程學系研究所碩士論文。
51. 黃威雄(2000),「應用類神經網路於颱風期間雷達降雨模擬之研究」,國立臺灣大學土木工程學系研究所碩士論文。
52. 經濟部水利署水利規劃試驗所(2007),「河川治理及環境營造規畫參考手冊」。
53. 經濟部水利署第四河川局(2013),「濁水溪流域整體治理綱要計畫(101至104年)」。
54. 經濟部水利署第四河川局(2016),「濁水溪整體疏濬評估計畫(106~108年)」。
55. 葉怡成(2000),「應用類神經網路-第二版」,儒林圖書有限公司。
56. 葉家勳(2009),「應用類神經網路推估河川懸浮載含量」,國立屏東科技大學水土保持系研究所碩士論文。
57. 蔡家淵(2018),「一維機率動床推估模式之發展與應用-以濁水溪為例」,國立交通大學土木工程學系研究所碩士論文。
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