1.Altug, S, M. Y. Chow, and H. J. Trussell, 1999, “Fuzzy Inference Systems Implemented on Neural Architectures for Motor Fault Detection and Diagnosis”, IEEE Trans. on Industrial Electronics, 46(6):1069-1079.
2.Atiya, A. F., & S. I. Shaheen, 1999, “A Comparison Between Neural- Network Forecasting Techniques-Case Study: River Flow Forecasting.” IEEE Trans. Neural Network, 10:402-409.
3.Becerikli, Y., A. F. Konar, T. Samad, 2003, “Intelligent Optimal Control with Dynamic Neural Networks”, Neural Networks, 16(2):251-259.
4.Beven, K., 1989. “Changing ideas in hydrology – the case of physically-based models”, J. of Hydrol., 105: 157-172.
5.Brazil, L. E. (1989) Multilevel calibration strategy for complex hydrologic simulation models. Technical Report NWS, No. 43, Natl. Oceanic and Atmos. Admin., Silver Spring, MD.
6.Cai, X. M., D. C. Mckinney, and L. S. Lasdon, 2001, “Solving Nonlinear Water Management Models Using A Combined Genetic Algorithm and Linear Programming Approach”, Advances in Water Resources, 24(6):667-676.
7.Cembrano G., Quevedo J., Salamero M., Puig V., Figueras J., Marti J., 2004, “Optimal control of urban drainage systems. A case study” Control Engineering Practice, 12(2004) 1-9.
8.Chaneau, J. L., M.Gunaratne and A. G.Altschaeffl, 1987, “An Application of Type-2 Sets to Decision Making in Engineering”, In Analysis of Fuzzy Information. II: Artificial Intelligence and Decision Systems (Bezdek J., Ed.). CRC. Boca Raton, FL
9.Chang, Fi-John, Kuo-Yuan Tseng, Paulo Chaves, 2007, “Shared near neighbours neural network model: a debris flow warning system”, Hydrological Processes, 21: 1968-1976
10.Chang, Fi-John, Ya-Ting Chang, 2006, “Adaptive neuro-fuzzy inference system for prediction of water level in reservoir”, Advances in Water Resources, 29: 1-10.
11.Chang, Fi-John, Yen-Chang Chen, 2003, “Estuary water-stage forecasting by using radial basis function neural network”, Journal of Hydrology, vol.270:158-166.
12.Chang F.J., Chang L.C., Wang Y.S., 2007, “Enforced Self-Organizing MapNeural Networks for River Flood Forecasting”, Hydrological Processes, 21: 741-749.
13.Chang F. J., H. F. Hu, & Y. C. Chen, 2001, “Counterpropagation fuzzy-neural network for streamflow reconstructing”, Hydrological Processes, 15(2): 219-232
14.Chang, F. J., K. Y. Chang, L. C., Chang, 2008, “Counterpropagation fuzzy-neural network for city flood control system”, Journal of Hydrology, 358: 24-34.
15.Chang F. J., L. C. Chang and H. L. Huang, 2002, “Real Time Recurrent Neural Network for Streamflow Forecasting”, Hydrological Processes, 16: 2577-2588.
16.Chang, F.J., L.C. Chang, & H.L. Huang, 2001,”Real Time Recurrent Neural Network for Streamflow Forecasting,” Hydrological Processes, vol.15.
17.Chang, F. J., L. Chen, 1998, “Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management”, Water Resource Management, EWRA, 12(3):185-198.
18.Chang, F. J., S. C. Hui, and Y. C. Chen, 2002, “Reservoir Operation Using Grey Fuzzy Stochastic Dynamic Programming”, Hydrological Processes, 16(12):2395-2408.
19.Chang F.J. , Yang H.C. , Lu J.Y. and Hong J.H. , 2008, “Neural Network Modeling for Mean Velocity and Turbulence Intensities of Steep Channel Flows”, Hydrological Processes, 22, 265–274
20.Chang F. J. and Y. C. Chen, 2003, “Estuary Water-Stage Forecasting by Using Radial Basis Function Neural Network”, Journal of Hydrology, 270: 158-165.
21.Chang F. J., and Y. C. Chen, 2001, “A counterpropagation fuzzy - neural network modeling approach to real-time streamflow prediction”, Journal of Hydrology, 245: pp153-164
22.Chang, F. J., and Y.-Y. Hwang, 1999. “A self-organization algorithm for real-time flood forecast”, Hydrological Processes, 13: 123-138.
23.Chang L. C., Chang F. J., 2001, “Intelligent control for modeling of real time reservoir operation”, Hydrological Processes, 15(9): 1621-1634.
24.Chang L. C., Chang F. J. and Chiang Y. M., 2003, “A Two-Step Ahead Recurrent Neural Network for Streamflow Forecasting”, Hydrological Processes.
25.Chang, Y. T., L. C. Chang and F. J. Chang, 2005, “Intelligent control for modeling of real time reservoir operation – Part Ⅱ:ANN with operating rule curves”, Hydrological Processes, 19: 1431-1444.
26.Chang, Y. T., L. C. Chang, and F. J. Chang, 2004, “Intelligent control for modeling of real-time reservoir operation- part II: ANN with operating rule curves”, Hydrological Processes.
27.Chaves P. and Chang F.J., 2008, “Intelligent Reservoir Operation System Based on Evolving Artificial Neural Networks”, Advances in Water Resources, Vol. 31 pp.926-936.
28.Cheng, C., 1999. “Fuzzy optimal model for the flood control system of the upper and middle reaches of the Yangtze River.”, Hydrological Sciences Journal, 44(4):573-582.
29.Cheng, C. T., Ou, C. P. and Chau, K. W. (2002) Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of Hydrology, 268(2002), 72-86.
30.Chen, Shen-Hsien, Yong-Huang Lin, Li-Chiu Chang, Fi-John Chang, 2006, “The strategy of building a flood forecast model by neuro-fuzzy network”, Hydrological Processes, 20: 1525-1540.
31.Chiang, Yen-Ming, Kuo-Lin Hsu, Fi-John Chang, Yang Hong, Soroosh Sorooshian, 2007, “Merging multiple precipitation sources for flash flood forecasting”, Journal of Hydrology, 340: 183-196.
32.Chiang Y. M., Chang F.J., Jou Ben Jong-Dao and Lin P.F., 2007, “Dynamic ANN for Precipitation Estimation and Forecasting from Radar Observations”, Journal of Hydrology, 334: 250-261.
33.Choi, K. S. and Ball, J. E. (2002) Parameter estimation for urban runoff modeling. Urban Water, 4(2002), 31-41.
34.Cooper, V. A., Nguyen, V. T. V., & Nicell, J. A. (1997) Evaluation of global optimization methods for conceptual rainfall-runoff model calibration. Wat. Sci. Tech., 36 (5), 53-60.
35.Coulibaly P., N. D. Evora, 2007, “Comparison of neural network methods for infilling missing daily weather records”, Journal of Hydrology, 341: 27-41.
36.Djukanovic, M. B., M. S. Calovic, B.V. Vesovic and D.J. Sobajic, 1997, “Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference”, IEEE Transactions on Energy Conversion, 12(4): 375-381
37.Duan, Q., Sorooshian, S. and Gupta, V. K. (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28(4), 1015-1031.
38.Duan, Q., Sorooshian, S. and Gupta, V. K. (1994) Optimal use of the SEC-UA global optimization method for calibrating watershed models. Journal of Hydrology, 158, 265-284.
39.Dutta S. and Shekhar S. 1988. Bond Rating: A Non-Conservative Application of Neural Networks. Proceedings of IEEE International Conference on Neural Networks. San Diego, CA. 2: 443-450.
40.Franchini, M., and Galeati, G. (1997) Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models. Hydrol. Sci. J., 42 (3), 357-379.
41.Fuh D. T. and Luo C. H. 2001. Unstable Morse code recognition system with back propagation neural network for person with disabilities. Journal of Medical Engineering & Technology. 25(3): 118-123.
42.Fukushima, K., 1988, “Neocognitron: A hierarchical neural network capable of visual pattern recognition”, Neural Networks, 1: 119-130
43.Goldberg, David E., “Genetic Algorithms in Search, Optimization, and Machine Learning.”, Addison-Wesley., 1997.
44.Gupta, V. K. and Sorooshian, S. (1985) The automatic calibration of conceptual catchment models using derivative-based optimization algorithms. Water Resources Research, 21(4), 437-485.
45.Ham F. M. and Kostanic I. 2001. Principles of Neurocomputing for Science & Engineering. McGraw-Hill: New York, NY.
46.Hasebe, M., and Y. Nagayama, 2002, “Reservoir operation using the neural network and fuzzy systems for dam control and operation support”, Advances in Engineering Software, 33(5)245-260.
47.Hecht-Nielsen R., 1987, “Counterpropagation Network”, Applied Optics, 26:4979-4984.
48.Hecht-Nielsen R., 1990, “Applications of Counterpropagation Networks”, Neural Networks, 1: 131-139.
49.Hendrickson, J. D., Sorooshian, S. and Brazil, L. E. (1988) comparison of Newton-type and direct search algorithms for calibration of conceptual rainfall-runoff models. Water Resources Research, 24(5), 691-700.
50.Holland, J. H., 1975, “Adaptation in Natural and Artificial Systems”, 2nd ed., Mass. Inst. of Technol., Cambridge.
51.Hopfield, J. J. 1982, “Neural networks and physical systems with emergent collective computational abilities”, Proceeding of the National Academy of Scientists, 79: 2554-2558
52.Hsu, K. L., H. V. Gupta, and S. Sorooshian, 1995, "Artificial Neural Network Modeling of the Rainfall-Runoff Process", Water Resour. Res., 31(10):2517-2530.
53.Ibbitt, R. P. and O’Donnell, T. (1971) Designing conceptual catchment models for automatic fitting methods. Proceedings of International Symposium on Mathematical Models in Hydrology, International Association of Hydrological Science, Warsaw.
54.Jain, S. K., A. Das, and D. K. Srivastava, 1999, “Application of ANN for reservoir inflow prediction and operation”, J. of Water Resources Planning and Management, 125(5):263-271.
55.Jang, J-S R., 1993, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Trans. On Syst. Man and Cyber., 23(3):665-685.
56.Jan Seibert, 2001, “On the need for benchmarks in hydrological modeling”, Hydrological Processes, 15: 1063-1064.
57.Johnston, P. R. and Pilgrim, D. H. (1976) Parameter optimization for watershed models. Water Resources Research, 12(3), 477-486.
58.Junhong Nie and D.A. Linkens, 1994, “Fast self-learning multivariable fuzzy controllers constructed from a modified CPN network”, International Journal of control, Vol.60, No.3, pp.369-393.
59.Junhong Nie, 1997, “Nonlinear time-series forecasting: A fuzzy-neural approach” Neurocomputing 16, ELSEVIER, pp.63-76 .
60.Junhong Nie and D.A.Linkens, 1995, Fuzzy-Neural Control: Principles, Algorithms and Applications, Prentice-Hall, London.
61.Jürgen Rahmel, 1996, “SplitNet: Learning of tree structured kohonen chains”, IEEE, pp.1221-1226.
62.Kohonen, T, 1998, Self-organization and associative memory, 2nd edition, Berlin, Germany: Springer-Verlag.
63.Komda, T., and Makarand C., 2000, “Hydrological forecasting using neural networks”, Journal of Hydrologic Engineering, 5(2):180-189.
64.Lallahem, S., J. Mania, A. Hani, Y. Najjar, 2005, “On the use of neural networks to evaluate groundwater levels in fractured media”, Journal of Hydrology, 307: 92-111.
65.Leahy P., Ger Kiely, Gearóid Corcoran, 2008,“Structural optimization and input selection of an artificial neural network for river level prediction”, Journal of Hydrology, 355: 192-201.
66.Liao H. P., J. P. Su, and H. M. Wu, 2001, “An application of ANFIS to modeling of a forecasting system for the demand of teacher human resources”, Journal of Education and Psychology, 24(1):1-17.
67.Liong, S. Y., Chan, W. T., and Jaya ShreeRam (1995) Peak-flow forecasting with genetic algorithm and SWMM. Journal of Hydraulic Engineering, August, 613-617.
68.Mahmut Firat, Mahmud Güngör, 2007, “River flow estimation using adaptive neuro fuzzy inference system”, Mathematics and Computers in Simulation, 75: 87-96.
69.McCulloch, W. S. and Pitts W., 1943, “A logical calculus of the ideas immanent in nervous activity”, Bulletin of Mathematical Biophysics, 5: 115-133.
70.Mendel, J. M., 2000, “Uncertainty, fuzzy logic, and signal processing”, Signal Processing. 80(6): 913-933
71.Munakata, T. and Y. Jani, 1994, “Fuzzy Systems: An Overview”, Communications of the ACM, 37(3): 69-76.
72.Nie, J., and D. A. Linkens, 1994, “Fast self-learning multivariable fuzzy controllers constructed from a modified CPN network. ”, Int. J. Control,. 60:369-393.
73.Nikolaos Vassilas, Patrick Thiran and Paolo Ienne, 1996, “On modifications of kohonen’s feature map algorithm for an efficient parallel implementation”, IEEE, pp.932-937.
74.Oonsivilai, A., and M. E. El-Hawary, 1999, “Power system dynamic load modeling using adaptive-network-based fuzzy inference system”, Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Eng., Shaw Conference Center, Edmonton, Alberta, Canada.
75.Pickup, G. (1977) Testing the efficiencies of algorithms and strategies for automatic calibration of rainfall-runoff models. Hydrological Sciences Bulletin, 22, 257-274.
76.Ponnambalam K., F. Karray, and S. J. Mousavi, 2003, “Minimizing variance of reservoir systems operations benefits using soft computing tools”, Fuzzy Sets and Systems, 139: 451-461
77.Raman, H. and V. Chandramouli, 1996, “Deriving a general operating policy for reservoirs using neural network”, Journal of Water Resources Planning and Management, 122(5): 342-347
78.Rumelhart D. E. and McClelland J. L. 1986. Parallel Distributed Processing Explorations in the Microstructure of Cognition. Vol. 1. Cambridge, MA: MIT Press.
79.Sajikumar, N. and B. S. Thandaveswara, 1999, “A non-linear rainfall-runoff model using an artificial network”, Journal of Hydrology, 216(1): 32-55
80.Sekeroglu B. 2004. Classification of Sonar Images Using Back Propagation Neural Network Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. 3092-3095.
81.Shu, C., T.B.M.J. Ouarda, 2008, “Regional flood frequency analysis at engaged sites using the adaptive neuro-fuzzy inference system”, Journal of Hydrology, 349: 31-43.
82.Solomatine, D. P. (1998). Genetic and other global optimization algorithms comparison and use in calibration problems. In V. Babovic, L. C. Larsen, Hydroinformatics'' 98 (pp. 1021-1028). Balkema: Rotterdam.
83.Sorooshian, S. and Gupta, V. K. (1983) Automatic calibration of conceptual rainfall-runoff models: the question of parameter observability and uniqueness. Water Resources Research, 19(1), 251-259.
84.Wang, Q. J., 1991, “The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models”, Water Resources Research, 27(9):2467-2471.
85.Wang, Q. J., 1997, Using genetic algorithms to optimize model parameters. Environ. Modeling and Software, 12 (1), 27-34.
86.Yang, Han-Chung, Fi-John Chang, 2005, “Modeling combined open channel flow by artificial neural networks”, Hydrological Processes, 19: 3747-3762.
87.Zadeh L. A. 1965. Fuzzy Sets. Information and Control. 8:338-353
88.Zadeh L. A. 1973. Outline of a New Approach to the Analysis of Complex System and Decision Processes. IEEE Transactions on Systems, Man and Cybernetics. SMC-3(1):28-44.
89.Zaman, S. and Ball, J. E. (1994). Simulation of small events in an urban catchment. Proceedings of 1994 hydrology and water resources conference (pp. 353–358), I.E. Australia, Adelaide, Australia, I.E. Australian National Conference Publication 94/15.
90.Zhu., M. L. and Fujita M., 1994, “Comparisons between fuzzy reasoning and neural network methods to forecast runoff discharge”, Journal of hydroscience and hydraulic engineering, 12(2):131-141.
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。92.王如意,2003,“動態遞迴式類神經網路之研究及其農業水資源之應用”,農業水利科技研究發展九十一年度成果發表討論論文集。
93.王如意,2000,「都會郊區降雨逕流模式之研究」,行政院國科會整合型計畫。
94.王如意、謝龍生、黃金龍,1999,「台北都會區淹水區域預測之研究,都會區降雨-逕流模式 之研究(二)」,行政院國家科學委員會專題研究計畫成果報告。
95.江俊生,2000,「都會區降雨-逕流模式之研究及其應用於抽水站水位之即時預報」,國立臺灣大學 農業工程學研究所碩士論文。96.向子菁,1999,「智慧型控制理論於水庫操作決策之研究」,國立台灣大學農業工程研究所碩士論文。97.余濬,2000,「都市雨水下水道設計模型之研究」,國立中央大學 土木工程研究所博士論文。98.李翁碩,2007,「抽水站水位預測及系統操作之研究」,國立台灣大學生物環境系統工程研究所碩士論文。99.林旭信,2004,「都市雨水下水道系統最佳化操作模擬」,國立台灣大學土木工程研究所博士論文。100.林國峰,2004,“應用全面監督式訓練法則所建立之輻狀基底函數網路於洪水流量預測”,水資源管理2004研討會,6-23~34。
101.邱建堯,2006,「結合GA與CG優選最佳倒傳遞類神經網路 --以雨水下水道水位預測模式為例」,國立台灣大學生物環境系統工程研究所碩士論文。102.邱昱禎,2003,「模糊規劃理論與優選法於水庫操作之研究」,國立台灣大學生物環境系統工程研究所碩士論文。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。104.孫建平,1996,「類神經網路及其應用於降雨及逕流過程之研究」,國立台灣大學農業工程研究所碩士論文。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。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。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。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。109.張斐章、許榮哲,1999,“灰色模糊動態規劃於水庫即時操作之應用”,台灣水利,47(1):44-53。
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。111.張斐章、梁晉銘,“類神經網路模糊推論模式在水文系統之研究”,台灣水利季刊,第50卷,第1期,第34-43頁,民國91年3月。
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。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。114.張斐章、黃金鐸、王文清,1995,“運用模糊序率動態規劃於水庫操作之研究”,台灣水利,43(4): 37-48。
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。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。117.張斐章、林莉,“遺傳演算法於專家系統中參數優選之研究”,農業工程學報,第39卷,第2期,pp.1-12,1993。
118.張斐章、張麗秋,2005,「類神經網路」,東華書局。
119.張麗秋,2001,「智慧型演算之類神經網路於水文系統」,國立臺灣大學農業工程學研究所博士論文。120.張雅婷,2005,「調適性網路模糊推論系統於水庫操作之研究」,國立臺灣大學農業工程學研究所博士論文。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。122.黃文政,2001,「模糊理論在河川流量預測及水資源情勢分析之研究(2/2)」,國家科學委員會研究報告。
123.黃文政,2000,「模糊理論在河川流量預測及水資源情勢分析之研究(1/2)」,國家科學委員會研究報告。
124.郭振泰、揚德良、簡振和,1990,「石門水庫操作模式與自動化監控系統規劃研究報告」,國立台灣大學水工試驗所。
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。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。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。128.陳莉,1995,「以物件導向之遺傳演算法優選水庫運用規線之研究」,國立臺灣大學農業工程學研究所博士論文。129.黃文政,吳建民,謝宏智,1994,“模糊聚類模式之應用”,第七屆水利工程研討會,pp. D21-D30。
130.黃文政、蔡坤良,1997,“水資源開發模糊模式之探討”,臺灣水利,45(2)。131.新建工程處,1989,「台北市雨水下水道規劃手冊」,台北市政府工務局。
132.養護工程處,1998,「抽水站人員操作維護訓練講習講義」,台北市政府工務局。
133.養護工程處,1984,「抽水站作業手冊」,台北市政府工務局。
134.蔡長泰、游保杉、周乃昉、游保杉,1993、1994、1995、1996,「地理資訊系統在淹水預警上之應用(1,2,3,4)」,台灣省水利局專題研究計畫。
135.蔡宗志,2000,「智慧型理論於水庫防洪操作之研究」,中華大學 土木工程研究所 碩士論文。136.萬象、陳昶憲、郭振泰,1990,“水庫防洪操作最佳控制模式之研究”,第五屆水利工程研討會,p.p.186-199。
137.簡錤彪,2003,「台北市防洪抽水站現況評估與聯合運轉可行性之探討」國立臺灣海洋大學碩士論文。138.謝龍生、許銘熙、許俊文、雷泰雄,2002,「強化都市淹水防救災業務運作機能之研究」,土木水利 第二十九卷,第二期,第21-34頁。139.蘇明道、李光敦、呂建華,1999,「淹水模式數值地理資訊系統之建立(一)」,88年度防災專案計畫成果研討論文集,第一冊,第11-1-16 頁。
140.蘇宗智,1974,「防洪幫浦設計之研究」,台北市政府工務局養護工程處。