Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications

Environmental Modelling and Software

Volumn 15, Issue 1, 2000, Pages 101-124

  Maier, Holger R ^a   Dandy, Graeme C ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Artificial neural networks; Forecasting; Model development; Modelling process; Prediction; Review; Water resources

Indexed keywords

COMPUTER ARCHITECTURE; FORECASTING; NEURAL NETWORKS;

WATER RESOURCE FORECASTING;

WATER RESOURCES;

ARTIFICIAL NEURAL NETWORK; ENVIRONMENTAL MODELING; RESOURCE MANAGEMENT; WATER RESOURCE;

EID: 0033957764     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1364-8152(99)00007-9     Document Type: Article

References (179)

1. Abu-Mostafa Y.S. The Vapnik-Chervonenkis dimension: Information versus complexity in learning. Neural Computation. 1(3):1989;312-317.
2. Allen G., le Marshall J.F. An evaluation of neural networks and discriminant analysis methods for application in operational rain forecasting. Australian Meteorological Magazine. 43(1):1994;17-28.
3. Amari S.-i., Murata N., Müller K.-R., Finke M., Yang H.H. Asymptotic statistical theory of overtraining and cross-validation. IEEE Transactions on Neural Networks. 8(5):1997;985-996.
4. Andrews R., Diederich J., Tickle A.B. A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems. 8:1995;373-389.
5. Angeline P.J., Sauders G.M., Pollack J.B. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks. 5:1994;54-65.
6. Bastarache D., El-Jabi N., Turkham N., Clair T.A. Predicting conductivity and acidity for small streams using neural networks. Canadian Journal of Civil Engineering. 24(6):1997;1030-1039.
7. Battiti R. Accelerated back-propagation learning: Two optimization methods. Complex Systems. 3:1989;331-342.
8. Battiti R. First- and second-order methods for learning: Between steepest descent and Newton's method. Neural Computation. 4:1992;141-166.
9. Bebis G., Georgiopoulos M. Feed-forward neural networks: Why network size is so important. IEEE Potentials. October/November:1994;27-31.
10. Bienenstock E., Geman S. Comment on 'Neural networks: A review from a statistical perspective' by B. Cheng and D.M. Titterington. Statistical Science. 9(1):1994;36-38.
11. Bingham, J.A.C., 1988. The Theory and Practice of Modem Design. Wiley, New York.
12. Box, G.E.P., Jenkins, G.M., 1976. Time Series Analysis, Forecasting and Control. Holden-Day Inc., San Francisco, CA.
13. Braddock, R.D., Kremmer, M.L., Sanzogni, L., 1997. Feed-forward artificial neural network model for forecasting rainfall run-off. Proceedings of the International Congress on Modelling and Simulation (Modsim 97), The Modelling and Simulation Society of Australia Inc., Hobart, Australia, pp. 1653-1658.
14. Breiman L. Comment on 'Neural networks: A review from a statistical perspective' by B. Cheng and D.M. Titterington. Statistical Science. 9(1):1994;38-42.
15. Broomhead D.S., Lowe D. Multivariate functional interpolation and adaptive networks. Complex Systems. 2:1988;321-355.
16. Burden F.R., Brereton R.G., Walsh P.T. Cross-validatory selection of test and validation sets in multivariate calibration and neural networks as applied to spectroscopy. Analyst. 122(10):1997;1015-1022.
17. Burke L.I., Ignizio J.P. Neural networks and operations research: an overview. Computer and Operations Research. 19(3/4):1992;179-189.
18. Castellano G., Fanelli A.M., Pelillo M. An iterative pruning algorithm for feedforward neural networks. IEEE Transactions on Neural Networks. 8(3):1997;519-531.
19. Chakraborty K., Mehrotra K., Mohan C.K., Ranka S. Forecasting the behaviour of multivariate time series using neural networks. Neural Networks. 5:1992;961-970.
20. Chatfield, C., 1975. The Analysis of Time Series: Theory and Practice. Chapman and Hall, London.
21. Chatfield C. Neural networks: Forecasting breakthrough or just a passing fad? International Journal of Forecasting. 9:1993;1-3.
22. Chen K., Yang L.P., Yu X., Chi H.S. A self-generating modular neural network architecture for supervised learning. Neurocomputing. 16(1):1997;33-48.
23. Cheng B., Titterington D.M. Neural networks: A review from a statistical perspective. Statistical Science. 9(1):1994;2-54.
24. Chng E.S., Chen S., Mulgrew B. Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Transactions on Neural Networks. 7(1):1996;191-194.
25. Chon K.H., Cohen R.J. Linear and nonlinear ARMA model parameter estimation using an artificial neural network. IEEE Transactions on Biomedical Engineering. 44(3):1997;168-174.
26. Chow T.W.S., Cho S.Y. Development of a recurrent sigma-pi neural network rainfall forecasting system in Hong Kong. Neural Computing and Applications. 5(2):1997;66-75.
27. Chung F.L., Lee T. A node pruning algorithm for backpropagation networks. International Journal of Neural Systems. 3(3):1992;301-314.
28. Clair T.A., Ehrman J.M. Variations in discharge and dissolved organic carbon and nitrogen export from terrestrial basins with changes in climate: a neural network approach. Limnology and Oceanography. 41(5):1996;921-927.
29. Connor J.T., Martin R.D., Atlas L.E. Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Networks. 5(2):1994;240-254.
30. Crespo J.L., Mora E. Drought estimation with neural networks. Advances in Engineering Software. 18(3):1993;167-170.
31. Dai H.C., Macbeth C. Effects of learning parameters on learning procedure and performance of a BPNN. Neural Networks. 10(8):1997;1505-1521.
32. Darken, C., Moody, J., 1990. Note on learning rate schedules for stochastic optimization. In: Lippmann, R.P., Moody, J.E., Touretzky, D.S. (Eds.), Advances in Neural Information Processing Systems 3. Morgan Kaufmann, San Mateo, CA.
33. Davis T.E., Principe J.C. A Markov chain framework for the simple genetic algorithm. Evolutionary Computation. 1(3):1993;269-288.
34. Dawson C.W., Wilby R. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal. 43(1):1998;47-66.
35. DeSilets L., Golden B., Wang Q., Kumar R. Predicting salinity in the Chesapeake Bay using backpropagation. Computer and Operations Research. 19(3/4):1992;227-285.
36. Doering A., Galicki M., Witte H. Structure optimization of neural networks with the A*-algorithm. IEEE Transactions on Neural Networks. 8(6):1997;1434-1445.
37. Elman J.L. Finding structure in time. Cognitive Science. 14:1990;179-211.
38. Fahlman, S.E., 1988. Faster-learning variations on back-propagation: An empirical study. 1988 Connectionist Models Summer School.
39. Fahlman, S.E., Lebiere, C., 1990. The cascade-correlation learning architecture. In: Touretzky, D.S. (Ed.), Advances in Neural Information Processing Systems 2. Morgan Kaufmann, San Mateo, CA.
40. Faraway J., Chatfield C. Time series forecasting with neural networks: a comparative study using the airline data. Applied Statistics. 47(2):1998;231-250.
41. Fernando D.A.K., Jayawardena A.W. Runoff forecasting using RBF networks with OLS algorithm. Journal of Hydrologic Engineering. 3(3):1998;203-209.
42. Finnhoff W., Hergert F., Zimmermann H.G. Improving model selection by nonconvergent methods. Neural Networks. 6:1993;771-783.
43. Flood I., Kartam N. Neural networks in civil engineering. I: Principles and understanding. Journal of Computing in Civil Engineering. 8(2):1994;131-148.
44. Fogel, L.J., Owens, A.J., Walsh, M.J., 1966. Artificial Intelligence Through Simulated Evolution. Wiley, New York.
45. Fortin V., Ouarda T.B.M.J., Bobée B. Comment on 'The use of artificial neural networks for the prediction of water quality parameters' by H.R. Maier and G.C. Dandy. Water Resources Research. 33(10):1997;2423-22424.
46. French M.N., Krajewski W.F., Cuykendall R.R. Rainfall forecasting in space and time using a neural network. Journal of Hydrology. 137:1992;1-31.
47. Gençay R., Liu T. Nonlinear modelling and prediction with feedforward and recurrent networks. Physica D. 108(1)(2):1997;119-134.
48. Gershenfeld, N.A., Weigend, A.S., 1994. The future of time series: Learning and understanding. In: Weigend, A.S., Gershenfeld, N.A. (Eds.), Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading, MA.
49. Gill, P., Murray, W., Wright, M., 1981. Practical Optimization. Academic Press, New York.
50. Goldberg, D., 1989. Genetic Algorithms. Addison-Wesley, Reading, MA.
51. Golden, R.M., 1996. Mathematical Methods for Neural Network Analysis and Design. MIT Press, Cambridge.
52. Golob R., Stokelj T., Grgic D. Neural-network-based water inflow forecasting. Control Engineering Practice. 6(5):1998;593-600.
53. Goswami P., Srividya A novel neural network design for long range prediction of rainfall pattern. Current Science. 70(6):1996;447-457.
54. Hansen J.V., Nelson R.D. Neural networks and traditional time series methods: A synergistic combination in state economic forecasts. IEEE Transactions on Neural Networks. 8(4):1997;863-873.
55. Hassoun, M.H., 1995. Fundamentals of Artificial Neural Networks. MIT Press, Cambridge.
56. Haugh L.D., Box G.E.P. Identification of dynamic regression (distributed lag) models connecting two time series. Journal of the American Statistical Association. 72(397):1977;121-130.
57. Hecht-Nielsen, R., 1987. Kolmogorov's mapping neural network existence theorem. Proceedings of the First IEEE International Joint Conference on Neural Networks, San Diego, California, pp. 11-14, IEEE, New York.
58. Hecht-Nielsen, R., 1990. Neurocomputing. Addison-Wesley, Reading, MA.
59. Heiss M., Kampl S. Multiplication-free radial basis function network. IEEE Transactions on Neural Networks. 7(6):1996;1461-1464.
60. Heskes, T.M., Kappen, B., 1993. On-line learning processes in artificial neural networks. In: Taylor, J.G. (Ed.), Mathematical Approaches to Neural Networks. Elsevier Science Publishers, Amsterdam.
61. Hill T., Marquez L., O'Connor M., Remus W. Artificial neural network models for forecasting and decision making. International Journal of Forecasting. 10:1994;5-15.
62. Hirose Y., Yamashita K., Hijiya S. Back-propagation algorithm which varies the number of hidden units. Neural Networks. 4(1):1991;61-66.
63. Hochreiter S., Schmidhuber J. Long short-term memory. Neural Computation. 9:1997;1735-1780.
64. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks. 2:1989;359-366.
65. Hsu K.L., Gao X.G., Sorooshian S., Gupta H.V. Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology. 36(9):1997;1176-1190.
66. Hsu K.-L., Gupta H.V., Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research. 31(10):1995;2517-2530.
67. Huang S.C., Huang Y.F. Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Transactions on Neural Networks. 2:1991;47-55.
68. Hwang J.T.G., Ding A.A. Prediction intervals for artificial neural networks. Journal of the American Statistical Association. 92(438):1997;748-757.
69. Irvine K.N., Eberhardt A.J. Multiplicative, seasonal ARIMA models for Lake Erie and Lake Ontario water levels. Water Resources Bulletin. 28(2):1992;385-396.
70. Jacobs R.A. Increased rates of convergence through learning rate adaptation. Neural Networks. 1:1988;295-307.
71. Jayawardena A.W., Fernando D.A.K. Use of radial basis function type artificial neural networks for runoff simulation. Computer-Aided Civil and Infrastructure Engineering. 13(2):1998;91-99.
72. Kaastra I., Boyd M.S. Forecasting futures trading volume using neural networks. The Journal of Futures Markets. 15(8):1995;953-970.
73. Kalman, B.L., Kwasny, S.C., 1992. Why Tanh? Choosing a sigmoidal function. In: Proceedings of the International Joint Conference on Neural Networks, Baltimore, MD IEEE, New York.
74. Karnin E.D. A simple procedure for pruning backpropagation trained neural networks. IEEE Transactions on Neural Networks. 1:1990;239-242.
75. Karunanithi N., Grenney W.J., Whitley D., Bovee K. Neural networks for river flow prediction. Journal of Computing in Civil Engineeirng. 8(2):1994;201-220.
76. Khotanzad A., Afkhami-Rohani R., Lu T.-L., Abaye A., Davis M., Maratukulam D.J. ANNSTLF - a neural-network-based electric load forecasting system. IEEE Transactions on Neural Networks. 8(4):1997;835-846.
77. Kirkpatrick S., Gilatt C.D., Vecchi M.P. Optimization by simulated annealing. Science. 220:1983;671-680.
78. Kollias S., Anastrassiou D. An adaptive least square algorithm for the efficient training of artificial neural networks. IEEE Transactions on Circuits and Systems. 36:1989;1092-1101.
79. Krishnapura V.G., Jutan A. ARMA neuron networks for modeling nonlinear dynamical systems. Canadian Journal of Chemical Engineering. 75(3):1997;574-582.
80. Kuligowski R.J., Barros A.P. Experiments in short-term precipitation forecasting using artificial neural networks. Monthly Weather Review. 126:1998;470-482.
81. Kuligowski R.J., Barros A.P. Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting. 13:1998;1194-1204.
82. Kuligowski R.J., Barros A.P. Using artificial neural networks to estimate missing rainfall data. Journal of the American Water Resources Association. 34(6):1998;1437-1447.
83. Kumar K.K. Optimization of the neural net connectivity pattern using a backpropagation algorithm. Neurocomputing. 5:1993;273-286.
84. Kwok T.-Y., Yeung D.-Y. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks. 8(3):1997;630-645.
85. Kwok T.-Y., Yeung D.-Y. Objective functions for training new hidden units in constructive neural networks. IEEE Transactions on Neural Networks. 8(5):1997;1131-1148.
86. Lachtermacher, G., Fuller, J.D., 1994. Backpropagation in hydrological time series forecasting. In: Hipel, K.W., McLeod, A.I., Panu, U.S., Singh, V.P. (Eds.), Stochastic and Statistical Methods in Hydrology and Environmental Engineering. Kluwer Academic, Dordrecht.
87. Lapedes A., Farber R. A self-optimizing, nonsymmetrical neural net for content addressable memory and pattern recognition. Physica D. 22:1986;247-259.
88. Lee B.W., Shen B.J. Hardware annealing in electronic neural networks. IEEE Transactions on Circuits and Systems. 38:1991;134-137.
89. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling. 90:1996;39-52.
90. Liano K. Robust error measure for supervised neural network learning with outliers. IEEE Transactions on Neural Networks. 7(1):1996;246-250.
91. Lin T., Horne B.G., Tiño P., Giles C.L. Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks. 7(6):1996;1329-1338.
92. Lippmann R.P. An introduction to computing with neural nets. IEEE Acoustics, Speech and Signal Processing Magazine. 4:1987;4-22.
93. Loke E., Warnaars E.A., Jacobsen P., Nelen F., Almeida M.D. Artificial neural networks as a tool in urban storm drainage. Water Science and Technology. 36(8)(9):1997;101-109.
94. Lorrai M., Sechi G.M. Neural nets for modelling rainfall-runoff transformations. Water Resources Management. 9(4):1995;299-313.
95. Ma S., Ji C.Y., Farmer J. An efficient EM-based training algorithm for feedforward neural networks. Neural Networks. 10(2):1997;243-256.
96. Maier, H.R., 1995. Use of artificial neural networks for modelling multivariate water quality time series. PhD Thesis, The University of Adelaide.
97. Maier H.R., Dandy G.C. Neural network models for forecasting univariate time series. Neural Network World. 6(5):1996;747-771.
98. Maier H.R., Dandy G.C. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research. 32(4):1996;1013-1022.
99. Maier H.R., Dandy G.C. Determining inputs for neural network models of multivariate time series. Microcomputers in Civil Engineering. 12(5):1997;353-368.
100. Maier H.R., Dandy G.C. Modelling cyanobacteria (blue-green algae) in the River Murray using artificial neural networks. Mathematics and Computers in Simulation. 43(3)(4)(5)(6):1997;377-386.
101. Maier H.R., Dandy G.C. The effect of internal parameters and geometry on the performance of back-propagation neural networks: An empirical study. Environmental Modelling and Software. 13(2):1998;193-209.
102. Maier H.R., Dandy G.C. Understanding the behaviour and optimising the performance of back-propagation neural networks: An empirical study. Environmental Modelling and Software. 13(2):1998;179-191.
103. Maier, H.R., Dandy, G.C., 1999a. Empirical comparison of various methods for training feedforward neural networks for salinity forecasting. Water Resources Research, submitted.
104. Maier, H.R., Dandy, G.C. 1999b. Neural network based modelling of environmental variables: A systematic approach. Mathematical and Computer Modelling, in press.
105. Maier H.R., Dandy G.C., Burch M.D. Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia. Ecological Modelling. 105(2/3):1998;257-272.
106. Maren, A., Harston, C., Pap, R., 1990. Handbook of Neural Computing Applications. Academic Press, San Diego, CA.
107. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, San Diego, CA.
108. McCulloch W.S., Pitts W. A logical calculus of the ideas imminent in nervous activity. Bulletin and Mathematical Biophysics. 5:1943;115-133.
109. Miller, G.F., Todd, P.M., Hedge, S.U., 1989. Designing neural networks using genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, Arlington, pp. 379-384. Morgan Kaufman, San Meteo.
110. Miller S.W. Rain rate estimation using neural networks. AI Applications. 11(1):1997;95-98.
111. Minns A.W., Hall M.J. Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal. 41(3):1996;399-417.
112. Møller M.S. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 6(4):1993;525-534.
113. Moody, J., Yarvin, N., 1992. Networks with learned unit response functions. In: Moody, J.E., Hanson, S.J., Lippmann, R.P. (Eds.), Advances in Neural Information Processing Systems 4. Morgan Kaufmann, San Mateo, CA.
114. Murata N., Yoshizawa S., Amari S. Network information criterion - Determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks. 5:1994;865-872.
115. Muttiah R.S., Srinivasan R., Allen P.M. Prediction of two-year peak stream discharges using neural networks. Journal of the American Water Resources Association. 33(3):1997;625-630.
116. Narendra K.S., Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks. 1:1990;4-27.
117. Nilsson, N., 1980. Principles of Artificial Intelligence. Springer Verlag, New York.
118. Nunnari G., Nucifora A.F.M., Randieri C. The application of neural techniques to the modelling of time-series of atmospheric pollution data. Ecological Modelling. 111(2)(3):1998;187-205.
119. Osowski S., Bojarczak P., Stodolski M. Fast second order learning algorithm for feedforward multilayer neural networks and its applications. Neural Networks. 9(9):1996;1583-1596.
120. Parisi R., Di Claudio E.D., Orlandi G., Rao B.D. A generalized learning paradigm exploiting the structure of feedforward neural networks. IEEE Transactions on Neural Networks. 7(6):1996;1451-1460.
121. Plaut D.C., Hinton G.E. Learning sets of filters using backpropagation. Comput. Speech Language. 2:1987;35-61.
122. Poff N.L., Tokar S., Johnson P. Stream hydrological and ecological responses to climate change assessed with an artificial neural network. Limnology and Oceanography. 41(5):1996;857-863.
123. Prechelt L. Connection pruning with static and adaptive pruning schedules. Neurocomputing. 16(1):1997;49-61.
124. Raman H., Sunilkumar N. Multivariate modelling of water resources time series using artificial neural networks. Hydrological Sciences Journal. 40(2):1995;145-163.
125. Recknagel F. ANNA - Artificial neural network model for predicting species abundance and succession of blue-green algae. Hydrobiologia. 349:1997;47-57.
126. Recknagel F., French M., Harkonen P., Yabunaka K.-I. Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling. 96:1997;11-28.
127. Reed R. Pruning algorithms - A review. IEEE Transactions on Neural Networks. 4(5):1993;740-747.
128. Refenes A., Burgess A.N., Bents Y. Neural networks in financial engineering: A study in methodology. IEEE Transactions on Neural Networks. 8(6):1997;1223-1267.
129. Ripley B.D. Neural networks and related methods of classification. Journal of the Royal Statistical Society B. 56(3):1994;409-456.
130. Roadknight C.M., Balls G.R., Mills G.E., Palmer-Brown D. Modeling complex environmental data. IEEE Transactions on Neural Networks. 8(4):1997;852-862.
131. Robbins H., Monro S. A stochastic approximation method. Annals of Mathematical Statistics. 22:1951;400-407.
132. Rogers L.L., Dowla F.U. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research. 30(2):1994;457-481.
133. Rojas, R., 1996. Neural Networks: A Systematic Introduction. Springer-Verlag, Berlin.
134. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986a. Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (Eds.), Parallel Distributed Processing. MIT Press, Cambridge.
135. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by backpropagating errors. Nature. 323:1986;533-536.
136. Sarle, W.S., 1994. Neural networks and statistical models. In: Proceedings of the Nineteenth Annual SAS Users Group International Conference, pp. 1538-1550. SAS Institute.
137. Schwarz G. Estimating the dimension of a model. Annals of Statistics. 6:1978;461-464.
138. Setiono R. A penalty-function approach for pruning feedforward neural networks. Neural Computation. 9(1):1997;185-204.
139. Setiono R., Hui L.C.K. Use of a quasi-Newton method in a feedforward neural-network construction algorithm. IEEE Transactions on Neural Networks. 6:1995;273-277.
140. Shamseldin A.Y. Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology. 199:1997;272-294.
141. Shanno D.F. Conjugate gradient methods with inexact line searches. Mathematics of Operations Research. 3:1978;244-256.
142. Shukla M.B., Kok R., Prasher S.O., Clark G., Lacroix R. Use of artificial neural networks in transient drainage design. Transactions of the ASAE. 39(1):1996;119-124.
143. Siegelmann H.T., Horne B.G., Giles C.L. Computational capabilities of recurrent NARX neural networks. IEEE Transactions on Systems Man and Cybernetics, Part B: Cybernetics. 27(2):1997;208-215.
144. Sietsma, J., Dow, R.J.F., 1988. Neural net pruning - Why and how. In: Proceedings of the IEEE International Conference on Neural Networks, San Diego, CA, pp. 325-333. IEEE, New York.
145. Sietsma J., Dow R.J.F. Creating artificial neural networks that generalize. Neural Networks. 4:1991;67-79.
146. Singhal, S., Wu, L., 1989. Training feedforward networks with the extended Kalman algorithm. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Scotland, pp. 1187-1190. IEEE, New York.
147. Smith J., Eli R.N. Neural-network models of rainfall-runoff processes. Journal of Water Resources Planning and Management. 121(6):1995;499-508.
148. Solla S.A., Levin E., Fleisher M. Accelerated learning in layered neural networks. Complex Systems. 2:1988;625-639.
149. Stone M. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society B. 36:1974;111-147.
150. Sureerattanan S., Phien H.N. Back-propagation networks for daily streamflow forecasting. Water Resources Journal. December:1997;1-7.
151. Tamura S.i., Tateishi M. Capabilities of a four-layered feedforward neural network: Four layers versus three. IEEE Transactions on Neural Networks. 8(2):1997;251-255.
152. Tawfik M., Ibrahim A., Fahmy H. Hysteresis sensitive neural network for modeling rating curves. Journal of Computing in Civil Engineering. 11(3):1997;206-211.
153. Thirumalaiah K., Deo M.C. Real-time flood forecasting using neural networks. Computer-Aided Civil and Infrastructure Engineering. 13(2):1998;101-111.
154. Thirumalaiah K., Deo M.C. River stage forecasting using artificial neural networks. Journal of Hydrologic Engineering. 3(1):1998;26-32.
155. Tibshirani R. Comment on 'Neural networks: A review from a statistical perspective' by B. Cheng and D.M. Titterington. Statistical Science. 9(1):1994;48-49.
156. Towell, G.G., Craven, M.K., Shavlik, J.W., 1991. Constructive induction in knowledge-based neural networks. In; Proceedings of the 8th International Workshop on Machine Learning, pp. 213-217. Morgan Kaufman, San Mateo.
157. Tsintikidis D., Haferman J.L., Anagnostou E.N., Krajewski W.F., Smith T.F. A neural network approach to estimating rainfall from spaceborne microwave data. IEEE Transactions on Geoscience and Remote Sensing. 35(5):1997;1079-1093.
158. Venkatesan C., Raskar S.D., Tambe S.S., Kulkarni B.D., Keshavamurty R.N. Prediction of all India summer monsoon rainfall using error-back-propagation neural networks. Meteorology and Atmospheric Physics. 62(3)(4):1997;225-240.
159. Verma B. Fast training of multilayer perceptrons. IEEE Transactions on Neural Networks. 8(6):1997;1314-1320.
160. Vicens G.J., Rodriguez-Iturbe I., Schaake J.C. Jr. A Bayesian framework for the use of regional information in hydrology. Water Resources Research. 11(3):1975;405-414.
161. Vitela J.E., Reifman J. Enhanced backpropagation training algorithm for transient event identification. Transactions of the American Nuclear Society. 69:1993;148-149.
162. Vitela J.E., Reifman J. Premature saturation in backpropagation networks - mechanism and necessary conditions. Neural Networks. 10(4):1997;721-735.
163. Von Lehman, A., Paek, E.G., Liao, P.F., Marrakchi, A., Patel, J.S., 1988. Factors influencing learning by back-propagation. In: Proceedings of the IEEE International Conference on Neural Networks, San Diego, CA, pp. 335-341. IEEE, New York.
164. Wang G.-J., Chen C.-C. A fast multilayer neural-network training algorithm based on the layer-by-layer optimizing procedures. IEEE Transactions on Neural Networks. 7(3):1996;768-775.
165. Warner B., Misra M. Understanding neural networks as statistical tools. American Statistician. 50(4):1996;284-293.
166. Weigend A.S., Rumelhart D.E., Huberman B.A. Predicting the future: A connectionist approach. International Journal of Neural Systems. 1(3):1990;193-209.
167. Wessels L., Barnard E. Avoiding false local minima by proper initialization of connections. IEEE Transactions on Neural Networks. 3(6):1992;899-905.
168. White H. Learning in artificial neural networks: A statistical perspective. Neural Computation. 1:1989;425-464.
169. Whitehead P.G., Howard A., Arulmani C. Modelling algal growth and transport in rivers - a comparison of time series analysis, dynamic mass balance and neural network techniques. Hydrobiologia. 349:1997;39-46.
170. Whitley R., Hromadka T.V. Approximate confidence intervals for design floods for a single site using a neural network. Water Resources Research. 35(1):1999;203-210.
171. Williams R.J., Zipser D. A learning algorithm for continually running fully recurrent networks. Neural Computation. 1:1989;270-280.
172. Xiao R.R., Chandrasekar V. Development of a neural network based algorithm for rainfall estimation from radar observations. IEEE Transactions on Geoscience and Remote Sensing. 35(1):1997;160-171.
173. Yabunaka K.-i., Hosomi M., Murakami A. Novel application of back-propagation artificial neural network model formulated to predict algal bloom. Water Science and Technology. 36(5):1997;89-97.
174. Yang C.C., Prasher C.O., Lacroix R. Applications of artificial neural networks to land drainage engineering. Transactions of the ASAE. 39(2):1996;525-533.
175. Yang L., Yu W. Backpropagation with homotopy. Neural Computation. 5(3):1993;363-366.
176. Yao X. A review of evolutionary artificial neural networks. Int. J. Intell. Syst. 8(4):1993;539-567.
177. Yao X., Liu Y. A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks. 8(3):1997;694-713.
178. Yu X.H., Chen G.A. Efficient backpropagation learning using optimal learning rate and momentum. Neural Networks. 10(3):1997;517-527.
179. Zhu, M.-L., Fujita, M., Hashimoto, N., 1994. Application of neural networks to runoff prediction. In: Hipel, K.W., McLeod, A.I., Panu, U.S., Singh, V.P. (Eds.), Stochastic and Statistical Methods in Hydrology and Environmental Engineering. Kluwer Academic, Dordrecht.