[Abrahart, R.J., See, L.M., Dawson, C.W., Shamseldin, A.Y., Wilby, R.L., 2010. Nearly Two Decades of Neural Network Hydrological Modeling. In: Sivakumar, B., Berndtsson, R., (Eds.): Advances in data-based approaches for hydrologic modeling and forecasting. World Scientific.10.1142/9789814307987_0006]Search in Google Scholar
[Arduino, G., Reggiani, P., Todini, E., 2005. Recent advances in flood forecasting and flood risk assessment. Hydrology and Earth System Sciences, 9, 4, 280-284.10.5194/hess-9-280-2005]Search in Google Scholar
[ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000a. Artificial neural networks in hydrology I: Preliminary concepts. J. Hydrol. Eng., 5, 2, 115-123.10.1061/(ASCE)1084-0699(2000)5:2(115)]Search in Google Scholar
[ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000b. Artificial neural networks in hydrology II: Hydrologic applications. J. Hydrol. Eng., 5, 2, 124-137.10.1061/(ASCE)1084-0699(2000)5:2(124)]Search in Google Scholar
[Birkundavyi, S., Labib, R., Trung, H.T., Rousselle, J., 2002. Performance of neural networks in daily streamflow forecasting. J. Hydrol. Eng., 7, 5, 392-398.10.1061/(ASCE)1084-0699(2002)7:5(392)]Search in Google Scholar
[Bowden, G.J., Dandy, G.C., Maier, H.R., 2005. Input determination for neural network models in water resources applications. Part 1-Background and methodology. J. Hydrol., 301, 1-4, 75-92.10.1016/j.jhydrol.2004.06.021]Search in Google Scholar
[Bruen, M., Yang, J., 2005. Functional networks in real-time flood forecasting-novel application. Advances in Water Resour., 28, 899-909.10.1016/j.advwatres.2005.03.001]Search in Google Scholar
[Campolo, M., Andreussi, P., Soldati, A., 1999. River flood forecasting with neural network model. Water Resour. Res., 35, 4, 1191-1197.10.1029/1998WR900086]Search in Google Scholar
[Corani, G., Guariso, G., 2005. An application of pruning in the design of neural networks for real rime flood forecasting. Neural Computation and Application, 14, 66-77.10.1007/s00521-004-0450-z]Search in Google Scholar
[Coulibaly, P., Anctil, F., Bobee, B., 2001. Multivariate reservoir inflow forecasting using temporal neural network. J. Hydrol. Eng., 6, 5, 367-376.10.1061/(ASCE)1084-0699(2001)6:5(367)]Search in Google Scholar
[Coulibaly, P., Anctil, F., Bobee, B., 2000. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol., 230, 244-257.10.1016/S0022-1694(00)00214-6]Search in Google Scholar
[Coulibaly, P., 2003. Impact of metrological predictions on realtime spring flow forecasting. Hydol. Process., 17, 3791-3801.10.1002/hyp.5168]Search in Google Scholar
[Coulibaly, P., Hache, M., Fortin, V., Bobee, B., 2005. Improving daily reservoir inflow forecasts with model combination. J. Hydrol., 10, 2, 91-99.10.1061/(ASCE)1084-0699(2005)10:2(91)]Search in Google Scholar
[Dawson, C.W., Wilby, R.L., 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography, 25, 1, 80-108.10.1191/030913301674775671]Search in Google Scholar
[Dawson, C.W., See, L.M., Abrahart, R.J., Heppenstall, A., 2006. Symbiotic adaptive neuro-evolution applied to rainfall- runoff modelling in northern England. Neural Networks, 19, 236-247. 10.1016/j.neunet.2006.01.009]Search in Google Scholar
[de Vos, N.J., Rientjes, T.H.M., 2005. Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation. Hydrology and Earth System Sciences, 9, 111-126.10.5194/hess-9-111-2005]Search in Google Scholar
[Duan, Q., Sorooshian, S., Gupta, V., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 28, 4, 1015-1031.10.1029/91WR02985]Search in Google Scholar
[French, M.N., Krajewski, W.F., Cuykendall, R.R., 1992. Rainfall forecasting in space and time using a neural network. J. Hydrol., 137, 1-31.10.1016/0022-1694(92)90046-X]Search in Google Scholar
[Gupta, V.K., Sorooshian, S., 1985. The relationship between data and the precision of parameter estimates of hydrologic models. J. Hydrol., 81, 1-2, 57-77.10.1016/0022-1694(85)90167-2]Search in Google Scholar
[Holland, J.H., 1975. Adaptation in Natural and Artificial System. Mass. Inst. of Technol. Cambridge.]Search in Google Scholar
[Hsu, K., Gupta, V.H., Sorroshian, S., 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res., 31, 10, 2517-2530.10.1029/95WR01955]Search in Google Scholar
[Chang, F.J., Chiang, Y.M., Chang, L.C., 2007. Multi-stepahead neural networks for flood forecasting. Hydrol. Sciences- J., 52, 1, 114-130.10.1623/hysj.52.1.114]Search in Google Scholar
[Chetan, M., Sudheer, K.P., 2006. A hybrid linear-neural model for river flow forecasting. Water Resour. Res., 42, 4, W04402.10.1029/2005WR004072]Search in Google Scholar
[Jain, A., Srinivasulu, S., 2004. Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resour. Res., 40, 1029-2003.10.1029/2003WR002355]Search in Google Scholar
[Jain, A., Sudheer, K.P., Srinivasulu, S., 2004. Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol. Process., 18, 3, 571-581.10.1002/hyp.5502]Search in Google Scholar
[Karunanithi, N., Grenney, W.J., Whitley, D., Bovee, K., 1994. Neural networks for river flow prediction. J. Computing in Civil Eng., 8, 2, 201-220.10.1061/(ASCE)0887-3801(1994)8:2(201)]Search in Google Scholar
[Khu, S.T., Liong, S.Y., Babovic, V., Madsen, H., Muttil, N., 2001. Genetic programming and its application in real-time runoff forecasting. J. Am. Water Resour. Assoc., 37, 2, 439-451.10.1111/j.1752-1688.2001.tb00980.x]Search in Google Scholar
[Kneale, P.E., See, L., Smith, A., 2001. Towards defining evaluation measures for neural network forecasting models. Proceedings of the Sixth International Conference on GeoComputation, University of Queensland, Australia, available at http://www.geocomputation.org/2001/papers/kneal.pdf (Last accessed on March 2, 2007).]Search in Google Scholar
[Kwok, T.Y., Yeung, D.Y., 1997. Objective functions for training new hidden units in constructiveneural networks. Neural Networks, IEEE, 8, 5, 1131-1148.10.1109/72.623214]Search in Google Scholar
[Le Cun, Y., Denker, J.S., Solla, S.A., 1990. Optimal brain damage. In: Advances in Neural Information Processing Systems 2. Morgan Kaufmann, San Mateo, California, pp. 598-605.]Search in Google Scholar
[Madsen, H., 2000. Automatic calibration of a conceptual rainfall- runoff model using multiple objectives. J. Hydrol., 235, 276-288.10.1016/S0022-1694(00)00279-1]Search in Google Scholar
[Madsen, H., Skotner, C., 2005. Adaptive state updating in realtime flow forecasting-a combined filtering and error forecasting procedure. J. Hydrol., 308, 302-312.10.1016/j.jhydrol.2004.10.030]Search in Google Scholar
[Madsen, H., Butts, M.B., Khu, S.T., Liong, S.Y., 2000. Data assimilation in rainfall-runoff forecasting, Hydroinformatics. 4th International Conference on Hydroinformatics, Cedar Rapids, Iowa, USA, 23-27.]Search in Google Scholar
[Maier, H.R., Dandy, G.C., 1997. Determining inputs for neural network models of multivariate time series. Microcomput. Civ. Eng., 12, 353- 368.10.1111/0885-9507.00069]Search in Google Scholar
[Maier, H.R., Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 15, 101-124.10.1016/S1364-8152(99)00007-9]Search in Google Scholar
[Maier, H.R., Jain, A., Dandy, G.C., Sudheer, K.P., 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25, 8, 891-909.10.1016/j.envsoft.2010.02.003]Search in Google Scholar
[Mathworks, 2004. Genetic algorithm and direct search tool box for use in MATLAB. The MathWorks, Inc., Natick, MA.]Search in Google Scholar
[McCuen, R.H., 2003. Modeling hydrologic change. CRC, Boca Raton, Fla.]Search in Google Scholar
[McCulloch, W.S., Pitts, W., 1943. A logic calculus of the ideas immanent in nervous activity. Bull. of Math. Biophys., 5, 115-133.10.1007/BF02478259]Search in Google Scholar
[Moore, R.J., 1986. Advances in real-time forecasting practice. Invited paper, Symposium on Flood Warning System, Winter Meeting of the River Enginnering Section, The Institution of Water Engineers and Scientists, 23 pp.]Search in Google Scholar
[Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models: 1. A discussion of principles. J. Hydrol., 10, 282-290.10.1016/0022-1694(70)90255-6]Search in Google Scholar
[Nayak, P.C., Sudheer, K.P., Rangan, D.M., Ramasastri, K.S., 2005. Short-term flood forecasting with a neurofuzzy model. Water Resour. Res., 41, 4, W04004.10.1029/2004WR003562]Search in Google Scholar
[Nayak, P.C., Sudheer, K.P., Jain, S.K., 2007. Rainfall-runoff modelling through hybrid intelligent system. Water Resour. Res. 43, 7, W07415.10.1029/2006WR004930]Search in Google Scholar
[Nazemi, A., Pookhadem, H.N., Mohammad, R., Akbarzadeh, T., Hosseini, S.M., 2003. Evolutionar neural network modeling for describing rainfall-runoff process. Hydrology Days, 224-235.]Search in Google Scholar
[Pan, T.Y., Wang, R.Y., 2004. State space neural networks for short term rainfall-runoff forecasting. J. Hydrol., 297, 32-50. 10.1016/j.jhydrol.2004.04.010]Search in Google Scholar
[Parasuraman, K., Elshorbagy, A., 2007. Cluster-Based Hydrologic Prediction Using Genetic Algorithm-Trained Neural Networks. J. Hydrol. Eng., 12, 1, 52-62.10.1061/(ASCE)1084-0699(2007)12:1(52)]Search in Google Scholar
[Refsgaard, J.C., 1997. Validation and intercomparison of different updating procedures for real-time forecasting. Nordic Hydrol., 28, 65-84.10.2166/nh.1997.0005]Search in Google Scholar
[Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by back-propagating errors. Nature, 323, 533-536.10.1038/323533a0]Search in Google Scholar
[Sajikumara, N., Thandaveswara, B.S., 1999. A non-linear rainfall- runoff model using an artificial neural network. J. Hydrol., 216, 32-55.10.1016/S0022-1694(98)00273-X]Search in Google Scholar
[Shamseldin, A.Y., 1997. Application of a neural network technique to rainfall runoff modelling. J. Hydrol., 199, 272-294.10.1016/S0022-1694(96)03330-6]Search in Google Scholar
[Shamseldin, A.Y., O’Connor, K.M., 2001. A non-linear neural network technique for updating of river flow forecasts. Hydrology and Earth System Sciences, 5, 4, 577-597.10.5194/hess-5-577-2001]Search in Google Scholar
[Sudheer, K.P., 2009. Real time flood forecasting by artificial neural network by extending the forecast lead time. Journal of Hydrological Research and Development, 24, 89-108.]Search in Google Scholar
[Sudheer, K.P., Gosain, A.K., Ramasastri, K.S., 2002. A datadriven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Process., 16, 1325-1330.10.1002/hyp.554]Search in Google Scholar
[Sudheer, K.P., Nayak, P.C., Ramasastri, K.S., 2003. Improving peak flow estimates in artificial neural network river flow models. Hydrol. Process., 17, 3, 677-686.10.1002/hyp.5103]Search in Google Scholar
[Thirumalaiah, K., Deo, M.C., 2000. Hydrological forecasting using neural networks, J. Hydrol. Eng., 5, 2, 180-189.10.1061/(ASCE)1084-0699(2000)5:2(180)]Search in Google Scholar
[Tokar, A.S., Johnson, A., 1999. Rainfall-runoff modeling using artifical neural networks. J. Hydrol. Eng., 4, 3, 232-239.10.1061/(ASCE)1084-0699(1999)4:3(232)]Search in Google Scholar
[US West Optical Company, 1989a. HYDRODATA: USGS daily and peak flows values. Denver, Colo.]Search in Google Scholar
[US West Optical Company, 1989b. CLIMATEDATA: Daily total rainfall and average temperature values. Denver, Colorado, USA.]Search in Google Scholar
[Wang, W., Van Gelder, P.H.A.J.M., Vrijling, J.K., Ma, J., 2006. Forecasting daily streamflow using hybrid ANN models. J. Hydrol., 324, 383-399.10.1016/j.jhydrol.2005.09.032]Search in Google Scholar
[WMO, 1992. Simulated real-time intercomparison of hydrological models. Operational Hydrology Report No. 38, World Meteorological Organization, Ganeva.]Search in Google Scholar
[Zealand, C.M., Burn, D.H., Simonovic, S.P., 1999. Short term streamflow forecasting using artificial neural networks. J. Hydrol., 214, 32-48. 10.1016/S0022-1694(98)00242-X]Search in Google Scholar