Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
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Abu-Mostafa, 1989, The Vapnik-Chervonenkis dimension: Information versus complexity in learning, Neural Computation, 1, 312, 10.1162/neco.1989.1.3.312
Allen, 1994, An evaluation of neural networks and discriminant analysis methods for application in operational rain forecasting, Australian Meteorological Magazine, 43, 17
Amari, 1997, Asymptotic statistical theory of overtraining and cross-validation, IEEE Transactions on Neural Networks, 8, 985, 10.1109/72.623200
Andrews, 1995, A survey and critique of techniques for extracting rules from trained artificial neural networks, Knowledge Based Systems, 8, 373, 10.1016/0950-7051(96)81920-4
Angeline, 1994, An evolutionary algorithm that constructs recurrent neural networks, IEEE Transactions on Neural Networks, 5, 54, 10.1109/72.265960
Bastarache, 1997, Predicting conductivity and acidity for small streams using neural networks, Canadian Journal of Civil Engineering, 24, 1030, 10.1139/cjce-24-6-1030
Battiti, 1989, Accelerated back-propagation learning: Two optimization methods, Complex Systems, 3, 331
Battiti, 1992, First- and second-order methods for learning: Between steepest descent and Newton's method, Neural Computation, 4, 141, 10.1162/neco.1992.4.2.141
Bebis, 1994, Feed-forward neural networks: Why network size is so important, IEEE Potentials, October/November, 27, 10.1109/45.329294
Bienenstock, 1994, Comment on `Neural networks: A review from a statistical perspective' by B. Cheng and D.M. Titterington, Statistical Science, 9, 36, 10.1214/ss/1177010641
Bingham, J.A.C., 1988. The Theory and Practice of Modem Design. Wiley, New York.
Box, G.E.P., Jenkins, G.M., 1976. Time Series Analysis, Forecasting and Control. Holden-Day Inc., San Francisco, CA.
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.
Breiman, 1994, Comment on `Neural networks: A review from a statistical perspective' by B. Cheng and D.M. Titterington, Statistical Science, 9, 38, 10.1214/ss/1177010642
Broomhead, 1988, Multivariate functional interpolation and adaptive networks, Complex Systems, 2, 321
Burden, 1997, Cross-validatory selection of test and validation sets in multivariate calibration and neural networks as applied to spectroscopy, Analyst, 122, 1015, 10.1039/a703565i
Burke, 1992, Neural networks and operations research: an overview, Computer and Operations Research, 19, 179, 10.1016/0305-0548(92)90043-5
Castellano, 1997, An iterative pruning algorithm for feedforward neural networks, IEEE Transactions on Neural Networks, 8, 519, 10.1109/72.572092
Chakraborty, 1992, Forecasting the behaviour of multivariate time series using neural networks, Neural Networks, 5, 961, 10.1016/S0893-6080(05)80092-9
Chatfield, 1993, Neural networks: Forecasting breakthrough or just a passing fad?, International Journal of Forecasting, 9, 1, 10.1016/0169-2070(93)90043-M
Chen, 1997, A self-generating modular neural network architecture for supervised learning, Neurocomputing, 16, 33, 10.1016/S0925-2312(96)00057-4
Cheng, 1994, Neural networks: A review from a statistical perspective, Statistical Science, 9, 2, 10.1214/ss/1177010638
Chng, 1996, Gradient radial basis function networks for nonlinear and nonstationary time series prediction, IEEE Transactions on Neural Networks, 7, 191, 10.1109/72.478403
Chon, 1997, Linear and nonlinear ARMA model parameter estimation using an artificial neural network, IEEE Transactions on Biomedical Engineering, 44, 168, 10.1109/10.554763
Chow, 1997, Development of a recurrent sigma-pi neural network rainfall forecasting system in Hong Kong, Neural Computing and Applications, 5, 66, 10.1007/BF01501172
Chung, 1992, A node pruning algorithm for backpropagation networks, International Journal of Neural Systems, 3, 301, 10.1142/S0129065792000231
Clair, 1996, 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, 921, 10.4319/lo.1996.41.5.0921
Connor, 1994, Recurrent neural networks and robust time series prediction, IEEE Transactions on Neural Networks, 5, 240, 10.1109/72.279188
Crespo, 1993, Drought estimation with neural networks, Advances in Engineering Software, 18, 167, 10.1016/0965-9978(93)90064-Z
Dai, 1997, Effects of learning parameters on learning procedure and performance of a BPNN, Neural Networks, 10, 1505, 10.1016/S0893-6080(97)00014-2
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.
Davis, 1993, A Markov chain framework for the simple genetic algorithm, Evolutionary Computation, 1, 269, 10.1162/evco.1993.1.3.269
Dawson, 1998, An artificial neural network approach to rainfall–runoff modelling, Hydrological Sciences Journal, 43, 47, 10.1080/02626669809492102
DeSilets, 1992, Predicting salinity in the Chesapeake Bay using backpropagation, Computer and Operations Research, 19, 227
Doering, 1997, Structure optimization of neural networks with the A*-algorithm, IEEE Transactions on Neural Networks, 8, 1434, 10.1109/72.641466
Fahlman, S.E., 1988. Faster-learning variations on back-propagation: An empirical study. 1988 Connectionist Models Summer School.
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.
Faraway, 1998, Time series forecasting with neural networks: a comparative study using the airline data, Applied Statistics, 47, 231
Fernando, 1998, Runoff forecasting using RBF networks with OLS algorithm, Journal of Hydrologic Engineering, 3, 203, 10.1061/(ASCE)1084-0699(1998)3:3(203)
Finnhoff, 1993, Improving model selection by nonconvergent methods, Neural Networks, 6, 771, 10.1016/S0893-6080(05)80122-4
Flood, 1994, Neural networks in civil engineering. I: Principles and understanding, Journal of Computing in Civil Engineering, 8, 131, 10.1061/(ASCE)0887-3801(1994)8:2(131)
Fogel, L.J., Owens, A.J., Walsh, M.J., 1966. Artificial Intelligence Through Simulated Evolution. Wiley, New York.
Fortin, 1997, 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, 2423, 10.1029/97WR00969
French, 1992, Rainfall forecasting in space and time using a neural network, Journal of Hydrology, 137, 1, 10.1016/0022-1694(92)90046-X
Gençay, 1997, Nonlinear modelling and prediction with feedforward and recurrent networks, Physica D, 108, 119, 10.1016/S0167-2789(97)82009-X
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.
Gill, P., Murray, W., Wright, M., 1981. Practical Optimization. Academic Press, New York.
Goldberg, D., 1989. Genetic Algorithms. Addison-Wesley, Reading, MA.
Golden, R.M., 1996. Mathematical Methods for Neural Network Analysis and Design. MIT Press, Cambridge.
Golob, 1998, Neural-network-based water inflow forecasting, Control Engineering Practice, 6, 593, 10.1016/S0967-0661(98)00037-9
Goswami, 1996, A novel neural network design for long range prediction of rainfall pattern, Current Science, 70, 447
Hansen, 1997, Neural networks and traditional time series methods: A synergistic combination in state economic forecasts, IEEE Transactions on Neural Networks, 8, 863, 10.1109/72.595884
Hassoun, M.H., 1995. Fundamentals of Artificial Neural Networks. MIT Press, Cambridge.
Haugh, 1977, Identification of dynamic regression (distributed lag) models connecting two time series, Journal of the American Statistical Association, 72, 121, 10.2307/2286919
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.
Hecht-Nielsen, R., 1990. Neurocomputing. Addison-Wesley, Reading, MA.
Heiss, 1996, Multiplication-free radial basis function network, IEEE Transactions on Neural Networks, 7, 1461, 10.1109/72.548173
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.
Hill, 1994, Artificial neural network models for forecasting and decision making, International Journal of Forecasting, 10, 5, 10.1016/0169-2070(94)90045-0
Hirose, 1991, Back-propagation algorithm which varies the number of hidden units, Neural Networks, 4, 61, 10.1016/0893-6080(91)90032-Z
Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8
Hsu, 1997, Precipitation estimation from remotely sensed information using artificial neural networks, Journal of Applied Meteorology, 36, 1176, 10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2
Hsu, 1995, Artificial neural network modeling of the rainfall–runoff process, Water Resources Research, 31, 2517, 10.1029/95WR01955
Huang, 1991, Bounds on the number of hidden neurons in multilayer perceptrons, IEEE Transactions on Neural Networks, 2, 47, 10.1109/72.80290
Hwang, 1997, Prediction intervals for artificial neural networks, Journal of the American Statistical Association, 92, 748, 10.2307/2965723
Irvine, 1992, Multiplicative, seasonal ARIMA models for Lake Erie and Lake Ontario water levels, Water Resources Bulletin, 28, 385, 10.1111/j.1752-1688.1992.tb04004.x
Jacobs, 1988, Increased rates of convergence through learning rate adaptation, Neural Networks, 1, 295, 10.1016/0893-6080(88)90003-2
Jayawardena, 1998, Use of radial basis function type artificial neural networks for runoff simulation, Computer-Aided Civil and Infrastructure Engineering, 13, 91, 10.1111/0885-9507.00089
Kaastra, 1995, Forecasting futures trading volume using neural networks, The Journal of Futures Markets, 15, 953, 10.1002/fut.3990150806
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.
Karnin, 1990, A simple procedure for pruning backpropagation trained neural networks, IEEE Transactions on Neural Networks, 1, 239, 10.1109/72.80236
Karunanithi, 1994, Neural networks for river flow prediction, Journal of Computing in Civil Engineeirng, 8, 201, 10.1061/(ASCE)0887-3801(1994)8:2(201)
Khotanzad, 1997, ANNSTLF—a neural-network-based electric load forecasting system, IEEE Transactions on Neural Networks, 8, 835, 10.1109/72.595881
Kirkpatrick, 1983, Optimization by simulated annealing, Science, 220, 671, 10.1126/science.220.4598.671
Kollias, 1989, An adaptive least square algorithm for the efficient training of artificial neural networks, IEEE Transactions on Circuits and Systems, 36, 1092, 10.1109/31.192419
Krishnapura, 1997, ARMA neuron networks for modeling nonlinear dynamical systems, Canadian Journal of Chemical Engineering, 75, 574, 10.1002/cjce.5450750311
Kuligowski, 1998, Experiments in short-term precipitation forecasting using artificial neural networks, Monthly Weather Review, 126, 470, 10.1175/1520-0493(1998)126<0470:EISTPF>2.0.CO;2
Kuligowski, 1998, Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks, Weather and Forecasting, 13, 1194, 10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2
Kuligowski, 1998, Using artificial neural networks to estimate missing rainfall data, Journal of the American Water Resources Association, 34, 1437, 10.1111/j.1752-1688.1998.tb05443.x
Kumar, 1993, Optimization of the neural net connectivity pattern using a backpropagation algorithm, Neurocomputing, 5, 273, 10.1016/0925-2312(93)90041-Z
Kwok, 1997, Constructive algorithms for structure learning in feedforward neural networks for regression problems, IEEE Transactions on Neural Networks, 8, 630, 10.1109/72.572102
Kwok, 1997, Objective functions for training new hidden units in constructive neural networks, IEEE Transactions on Neural Networks, 8, 1131, 10.1109/72.623214
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.
Lapedes, 1986, A self-optimizing, nonsymmetrical neural net for content addressable memory and pattern recognition, Physica D, 22, 247, 10.1016/0167-2789(86)90244-7
Lee, 1991, Hardware annealing in electronic neural networks, IEEE Transactions on Circuits and Systems, 38, 134, 10.1109/31.101312
Lek, 1996, Application of neural networks to modelling nonlinear relationships in ecology, Ecological Modelling, 90, 39, 10.1016/0304-3800(95)00142-5
Liano, 1996, Robust error measure for supervised neural network learning with outliers, IEEE Transactions on Neural Networks, 7, 246, 10.1109/72.478411
Lin, 1996, Learning long-term dependencies in NARX recurrent neural networks, IEEE Transactions on Neural Networks, 7, 1329, 10.1109/72.548162
Lippmann, 1987, An introduction to computing with neural nets, IEEE Acoustics, Speech and Signal Processing Magazine, 4, 4
Loke, 1997, Artificial neural networks as a tool in urban storm drainage, Water Science and Technology, 36, 101, 10.1016/S0273-1223(97)00612-4
Lorrai, 1995, Neural nets for modelling rainfall–runoff transformations, Water Resources Management, 9, 299, 10.1007/BF00872489
Ma, 1997, An efficient EM-based training algorithm for feedforward neural networks, Neural Networks, 10, 243, 10.1016/S0893-6080(96)00049-4
Maier, H.R., 1995. Use of artificial neural networks for modelling multivariate water quality time series. PhD Thesis, The University of Adelaide.
Maier, 1996, Neural network models for forecasting univariate time series, Neural Network World, 6, 747
Maier, 1996, The use of artificial neural networks for the prediction of water quality parameters, Water Resources Research, 32, 1013, 10.1029/96WR03529
Maier, 1997, Determining inputs for neural network models of multivariate time series, Microcomputers in Civil Engineering, 12, 353, 10.1111/0885-9507.00069
Maier, 1997, Modelling cyanobacteria (blue-green algae) in the River Murray using artificial neural networks, Mathematics and Computers in Simulation, 43, 377, 10.1016/S0378-4754(97)00022-0
Maier, 1998, The effect of internal parameters and geometry on the performance of back-propagation neural networks: An empirical study, Environmental Modelling and Software, 13, 193, 10.1016/S1364-8152(98)00020-6
Maier, 1998, Understanding the behaviour and optimising the performance of back-propagation neural networks: An empirical study, Environmental Modelling and Software, 13, 179, 10.1016/S1364-8152(98)00019-X
Maier, H.R., Dandy, G.C., 1999a. Empirical comparison of various methods for training feedforward neural networks for salinity forecasting. Water Resources Research, submitted.
Maier, H.R., Dandy, G.C. 1999b. Neural network based modelling of environmental variables: A systematic approach. Mathematical and Computer Modelling, in press.
Maier, 1998, Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia, Ecological Modelling, 105, 257, 10.1016/S0304-3800(97)00161-0
Maren, A., Harston, C., Pap, R., 1990. Handbook of Neural Computing Applications. Academic Press, San Diego, CA.
McCulloch, 1943, A logical calculus of the ideas imminent in nervous activity, Bulletin and Mathematical Biophysics, 5, 115, 10.1007/BF02478259
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.
Miller, 1997, Rain rate estimation using neural networks, AI Applications, 11, 95
Minns, 1996, Artificial neural networks as rainfall–runoff models, Hydrological Sciences Journal, 41, 399, 10.1080/02626669609491511
Møller, 1993, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 525, 10.1016/S0893-6080(05)80056-5
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.
Murata, 1994, Network information criterion—Determining the number of hidden units for an artificial neural network model, IEEE Transactions on Neural Networks, 5, 865, 10.1109/72.329683
Muttiah, 1997, Prediction of two-year peak stream discharges using neural networks, Journal of the American Water Resources Association, 33, 625, 10.1111/j.1752-1688.1997.tb03537.x
Narendra, 1990, Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks, 1, 4, 10.1109/72.80202
Nilsson, N., 1980. Principles of Artificial Intelligence. Springer Verlag, New York.
Nunnari, 1998, The application of neural techniques to the modelling of time-series of atmospheric pollution data, Ecological Modelling, 111, 187, 10.1016/S0304-3800(98)00118-5
Osowski, 1996, Fast second order learning algorithm for feedforward multilayer neural networks and its applications, Neural Networks, 9, 1583, 10.1016/S0893-6080(96)00029-9
Parisi, 1996, A generalized learning paradigm exploiting the structure of feedforward neural networks, IEEE Transactions on Neural Networks, 7, 1451, 10.1109/72.548172
Plaut, 1987, Learning sets of filters using backpropagation, Comput. Speech Language, 2, 35, 10.1016/0885-2308(87)90026-X
Poff, 1996, Stream hydrological and ecological responses to climate change assessed with an artificial neural network, Limnology and Oceanography, 41, 857, 10.4319/lo.1996.41.5.0857
Prechelt, 1997, Connection pruning with static and adaptive pruning schedules, Neurocomputing, 16, 49, 10.1016/S0925-2312(96)00054-9
Raman, 1995, Multivariate modelling of water resources time series using artificial neural networks, Hydrological Sciences Journal, 40, 145, 10.1080/02626669509491401
Recknagel, 1997, ANNA—Artificial neural network model for predicting species abundance and succession of blue-green algae, Hydrobiologia, 349, 47, 10.1023/A:1003041427672
Recknagel, 1997, Artificial neural network approach for modelling and prediction of algal blooms, Ecological Modelling, 96, 11, 10.1016/S0304-3800(96)00049-X
Reed, 1993, Pruning algorithms—A review, IEEE Transactions on Neural Networks, 4, 740, 10.1109/72.248452
Refenes, 1997, Neural networks in financial engineering: A study in methodology, IEEE Transactions on Neural Networks, 8, 1223, 10.1109/72.641449
Ripley, 1994, Neural networks and related methods of classification, Journal of the Royal Statistical Society B, 56, 409
Roadknight, 1997, Modeling complex environmental data, IEEE Transactions on Neural Networks, 8, 852, 10.1109/72.595883
Robbins, 1951, A stochastic approximation method, Annals of Mathematical Statistics, 22, 400, 10.1214/aoms/1177729586
Rogers, 1994, Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling, Water Resources Research, 30, 457, 10.1029/93WR01494
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.
Rumelhart, 1986, Learning representations by backpropagating errors, Nature, 323, 533, 10.1038/323533a0
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.
Schwarz, 1978, Estimating the dimension of a model, Annals of Statistics, 6, 461, 10.1214/aos/1176344136
Setiono, 1997, A penalty-function approach for pruning feedforward neural networks, Neural Computation, 9, 185, 10.1162/neco.1997.9.1.185
Setiono, 1995, Use of a quasi-Newton method in a feedforward neural-network construction algorithm, IEEE Transactions on Neural Networks, 6, 273, 10.1109/72.363426
Shamseldin, 1997, Application of a neural network technique to rainfall–runoff modelling, Journal of Hydrology, 199, 272, 10.1016/S0022-1694(96)03330-6
Shanno, 1978, Conjugate gradient methods with inexact line searches, Mathematics of Operations Research, 3, 244, 10.1287/moor.3.3.244
Shukla, 1996, Use of artificial neural networks in transient drainage design, Transactions of the ASAE, 39, 119, 10.13031/2013.27488
Siegelmann, 1997, Computational capabilities of recurrent NARX neural networks, IEEE Transactions on Systems Man and Cybernetics, Part B: Cybernetics, 27, 208, 10.1109/3477.558801
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.
Sietsma, 1991, Creating artificial neural networks that generalize, Neural Networks, 4, 67, 10.1016/0893-6080(91)90033-2
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.
Smith, 1995, Neural-network models of rainfall–runoff processes, Journal of Water Resources Planning and Management, 121, 499, 10.1061/(ASCE)0733-9496(1995)121:6(499)
Solla, 1988, Accelerated learning in layered neural networks, Complex Systems, 2, 625
Stone, 1974, Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society B, 36, 111
Sureerattanan, 1997, Back-propagation networks for daily streamflow forecasting, Water Resources Journal, December, 1
Tamura, 1997, Capabilities of a four-layered feedforward neural network: Four layers versus three, IEEE Transactions on Neural Networks, 8, 251, 10.1109/72.557662
Tawfik, 1997, Hysteresis sensitive neural network for modeling rating curves, Journal of Computing in Civil Engineering, 11, 206, 10.1061/(ASCE)0887-3801(1997)11:3(206)
Thirumalaiah, 1998, Real-time flood forecasting using neural networks, Computer-Aided Civil and Infrastructure Engineering, 13, 101, 10.1111/0885-9507.00090
Thirumalaiah, 1998, River stage forecasting using artificial neural networks, Journal of Hydrologic Engineering, 3, 26, 10.1061/(ASCE)1084-0699(1998)3:1(26)
Tibshirani, 1994, Comment on `Neural networks: A review from a statistical perspective' by B. Cheng and D.M. Titterington, Statistical Science, 9, 48, 10.1214/ss/1177010645
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.
Tsintikidis, 1997, A neural network approach to estimating rainfall from spaceborne microwave data, IEEE Transactions on Geoscience and Remote Sensing, 35, 1079, 10.1109/36.628775
Venkatesan, 1997, Prediction of all India summer monsoon rainfall using error-back-propagation neural networks, Meteorology and Atmospheric Physics, 62, 225, 10.1007/BF01029704
Verma, 1997, Fast training of multilayer perceptrons, IEEE Transactions on Neural Networks, 8, 1314, 10.1109/72.641454
Vicens, 1975, A Bayesian framework for the use of regional information in hydrology, Water Resources Research, 11, 405, 10.1029/WR011i003p00405
Vitela, 1993, Enhanced backpropagation training algorithm for transient event identification, Transactions of the American Nuclear Society, 69, 148
Vitela, 1997, Premature saturation in backpropagation networks—mechanism and necessary conditions, Neural Networks, 10, 721, 10.1016/S0893-6080(96)00117-7
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.
Wang, 1996, A fast multilayer neural-network training algorithm based on the layer-by-layer optimizing procedures, IEEE Transactions on Neural Networks, 7, 768, 10.1109/72.501734
Warner, 1996, Understanding neural networks as statistical tools, American Statistician, 50, 284, 10.2307/2684922
Weigend, 1990, Predicting the future: A connectionist approach, International Journal of Neural Systems, 1, 193, 10.1142/S0129065790000102
Wessels, 1992, Avoiding false local minima by proper initialization of connections, IEEE Transactions on Neural Networks, 3, 899, 10.1109/72.165592
White, 1989, Learning in artificial neural networks: A statistical perspective, Neural Computation, 1, 425, 10.1162/neco.1989.1.4.425
Whitehead, 1997, Modelling algal growth and transport in rivers—a comparison of time series analysis, dynamic mass balance and neural network techniques, Hydrobiologia, 349, 39, 10.1023/A:1003089310834
Whitley, 1999, Approximate confidence intervals for design floods for a single site using a neural network, Water Resources Research, 35, 203, 10.1029/1998WR900016
Williams, 1989, A learning algorithm for continually running fully recurrent networks, Neural Computation, 1, 270, 10.1162/neco.1989.1.2.270
Xiao, 1997, Development of a neural network based algorithm for rainfall estimation from radar observations, IEEE Transactions on Geoscience and Remote Sensing, 35, 160, 10.1109/36.551944
Yabunaka, 1997, Novel application of back-propagation artificial neural network model formulated to predict algal bloom, Water Science and Technology, 36, 89, 10.1016/S0273-1223(97)00464-2
Yang, 1996, Applications of artificial neural networks to land drainage engineering, Transactions of the ASAE, 39, 525, 10.13031/2013.27531
Yao, 1993, A review of evolutionary artificial neural networks, Int. J. Intell. Syst., 8, 539, 10.1002/int.4550080406
Yao, 1997, A new evolutionary system for evolving artificial neural networks, IEEE Transactions on Neural Networks, 8, 694, 10.1109/72.572107
Yu, 1997, Efficient backpropagation learning using optimal learning rate and momentum, Neural Networks, 10, 517, 10.1016/S0893-6080(96)00102-5