REFERENCES

• Abrahart, R.J., Heppenstall, A.J., and See, L.M., 2007. Timing error correction procedures applied to neural network rainfall–runoff modelling. Hydrological Sciences Journal, 52 (3), 414–431.
   Web of Science ®Google Scholar
• Adamowski, J., 2007. Development of a short-term river flood forecasting method based on wavelet analysis. Warsaw: Polish Academy Sciences Monograph.
   Google Scholar
• Adamowski, J., 2008a. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology, 353, 247–266.
   Web of Science ®Google Scholar
• Adamowski, J., 2008b. River flow forecasting using wavelet and cross-wavelet transform models. Hydrological Processes, 22, 4877–4891.
   Web of Science ®Google Scholar
• Adamowski, J. and Sun, K., 2010. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390, 85–91.
   Web of Science ®Google Scholar
• Anctil, F. and Tape, D.G., 2004. An exploration of artificial neural network rainfall–runoff forecasting combined with wavelet decomposition. Journal of Environmental Engineering and Science, 3 (s1), s121–s128.
   Web of Science ®Google Scholar
• ASCE Task Committee, 2000a. Artificial neural networks in Hydrology. I: preliminary concepts. Journal of Hydrologic Engineering, 5 (2), 115–123.
   Web of Science ®Google Scholar
• ASCE Task Committee, 2000b. Artificial neural networks in hydrology II: hydrologic applications. Journal of Hydrologic Engineering, 5 (2), 124–132.
   Web of Science ®Google Scholar
• Besaw, L.E., et al., 2010. Advances in ungauged streamflow prediction using artificial neural networks. Journal of Hydrology, 386, 27–37.
   Web of Science ®Google Scholar
• Birikundavyi, S., et al., 2002. Performance of neural networks in daily streamflow forecasting. Journal of Hydrologic Engineering, 7 (5), 392–398.
   Web of Science ®Google Scholar
• Bowden, G.J., Maier, H.R., and Dandy, G.C., 2005. Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. Journal of Hydrology, 301, 93–107.
   Web of Science ®Google Scholar
• Cannas, B., et al., 2006. River flow forecasting using neural networks and wavelet analysis. In: Proceedings of the European Geosciences Union. Munich: EGU Press.
   Google Scholar
• Chandramouli, V., et al., 2007. Backfilling missing microbial concentrations in a riverine database using artificial neural networks. Water Research, 41 (1), 217–227.
   PubMed Web of Science ®Google Scholar
• Cigizoglu, H.K., 2004. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in Water Resources, 27 (2), 185–195.
   Web of Science ®Google Scholar
• Coulibaly, P. and Evora, N.D., 2007. Comparison of neural network methods for infilling missing daily weather records. Journal of Hydrology, 341, 27–41.
   Web of Science ®Google Scholar
• Daliakopoulos, I.N., Coulibaly, P., and Tsanis, I.K., 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309 (1–4), 229–240.
   Web of Science ®Google Scholar
• Daubechies, I., 1992. Ten lectures on wavelets. Philadelphia, PA: SIAM.
   Google Scholar
• Dawson, C.W., et al., 2006. Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology, 319 (1–4), 391–409.
   Web of Science ®Google Scholar
• Dawson, C.W., Abrahart, R.J., and See, L.M., 2007. HydroTest: a web-based toolbox of statistical measures for the standardised assessment of hydrological forecasts. Environmental Modelling & Software, 27, 1034–1052.
   Web of Science ®Google Scholar
• Dawson, C.W. and Wilby, R.L., 1999. A comparison of artificial neural networks used for river forecasting. Hydrology and Earth System Sciences, 3 (4), 529–540.
   Web of Science ®Google Scholar
• Demuth, H. and Beale, M., 2005. Neural network toolbox: for use with Matlab. Natick, MA: The MathWorks.
   Google Scholar
• De Vos, N.J. and 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.
   Web of Science ®Google Scholar
• Elshorbagy, A. and Parasuraman, K., 2008. On the relevance of using artificial neural networks for estimating soil moisture content. Journal of Hydrology, 362, 1–18.
   Web of Science ®Google Scholar
• Farias, C.A.S., Santos, C.A.G., and Celeste, A.B., 2011. Daily reservoir operating rules by implicit stochastic optimization and artificial neural networks in a semi-arid land of Brazil. In: G. Blöschl, et al., eds. Risk in water resources management. Wallingford: IAHS Press, IAHS Publication. 347, 191–197.
   Google Scholar
• Feng, S., et al., 2008. Neural networks to simulate regional ground water levels affected by human activities. Ground Water, 46 (1), 80–90.
   PubMed Web of Science ®Google Scholar
• Guilhon, L.G.F., Rocha, V.F., and Moreira, J.C., 2007. Comparação de métodos de previsão de vazões naturais afluentes a aproveitamentos hidrelétricos. Revista Brasileira de Recursos Hídricos, 12 (3), 13–20.
   Google Scholar
• Haykin, S., 2005. Neural networks: a comprehensive foundation. New Delhi: Prentice-Hall.
   Google Scholar
• He, J., et al., 2011. Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection. Journal of Hydrology, 400, 10–23.
   Web of Science ®Google Scholar
• Jain, A. and 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 Resources Research, 40, W04302.
   Web of Science ®Google Scholar
• Jayawardena, A.W. and Fernando, T.M.K.G., 2001. River flow prediction: an artificial neural network approach. In: A.H. Schumann, et al., eds. Regional management of water resources. Wallingford: IAHS Press, IAHS Publication. 268, 239–245.
   Google Scholar
• Joorabchi, A., Zhang, H., and Blumenstein, M.M., 2007. Application of artificial neural networks in flow discharge prediction for the Fitzroy River, Australia. Journal Coastal Research, 50, 287–291.
   Google Scholar
• Kentel, E., 2009. Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates. Journal of Hydrology, 375, 481–488.
   Web of Science ®Google Scholar
• Kerh, T. and Lee, C.S., 2006. Neural networks forecasting of flood discharge at an unmeasured station using river upstream information. Advances in Engineering Software, 37 (8), 533–543.
   Web of Science ®Google Scholar
• Kim, T.-W. and Valdés, J.B., 2003. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8 (6), 319–328.
   Web of Science ®Google Scholar
• Kisi, Ö., 2007. Streamflow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering, 12 (5), 532–539.
   Web of Science ®Google Scholar
• Kisi, Ö., 2008. Stream flow forecasting using neuro-wavelet technique. Hydrological Processes, 22 (20), 4142–4152.
   Web of Science ®Google Scholar
• Kisi, Ö., 2009. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering, 14 (8), 773–782.
   Web of Science ®Google Scholar
• Maier, H.R. and 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 (1), 101–124.
   Web of Science ®Google Scholar
• Mallat, S., 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions Pattern Analysis and Machine Intelligence, 11 (7), 674–693.
   Web of Science ®Google Scholar
• Muttil, N. and Chau, K.-W., 2006. Neural network and genetic programming for modelling coastal algal blooms. International Journal of Environment and Pollution, 28 (3/4), 223–238.
   Web of Science ®Google Scholar
• Napolitano, G., Serinaldi, F., and See, L., 2011. Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination. Journal of Hydrology, 406 (3–4), 199–214.
   Web of Science ®Google Scholar
• Partal, T., 2009. River flow forecasting using different artificial neural network algorithms and wavelet transform. Canadian Journal of Civil Engineering, 36 (1), 26–38.
   Web of Science ®Google Scholar
• Pulido-Calvo, I. and Portela, M.N., 2007. Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. Journal of Hydrology, 332, 1–15.
   Web of Science ®Google Scholar
• Rajurkar, M.P., Kothyari, U.C., and Chaube, U.C., 2004. Modeling of the daily rainfall–runoff relationship with artificial neural network. Journal of Hydrology, 285, 96–113.
   Web of Science ®Google Scholar
• Santos, C.A.G. and Morais, B.S., 2013. Identification of precipitation zones within São Francisco River basin by global wavelet power spectra. Hydrological Sciences Journal, 58 (4), 789–796. doi:10.1080/02626667.2013.778412.
   Web of Science ®Google Scholar
• Santos, C.A.G., Morais, B.S., and Silva, G.B.L., 2009. Drought forecast using artificial neural network for three hydrological zones in San Francisco river basin. In: K.K. Yilmaz, et al., eds. New approaches to hydrological prediction in data-sparse regions. Wallingford: IAHS Press, IAHS Publication. 333, 302–312.
   Google Scholar
• Sarmiento, F.P. and Neira, N.O., 2009. Forecasting of monthly streamflows based on artificial neural network. Journal of Hydrologic Engineering, 14 (12), 1390–1395.
   Web of Science ®Google Scholar
• Shrestha, R.R., Theobald, S., and Nestmann, F., 2005. Simulation of flood flow in a river system using artificial neural networks. Hydrology and Earth System Sciences, 9 (4), 313–321.
   Web of Science ®Google Scholar
• Singh, K.P., et al., 2009. Artificial neural network modeling of the river water quality—a case study. Ecological Modelling, 220 (6), 888–895.
   Web of Science ®Google Scholar
• Turan, M.E. and Yurdusev, M.A., 2009. River flow estimation from upstream flow records by artificial intelligence methods. Journal of Hydrology, 369, 71–77.
   Web of Science ®Google Scholar
• Wang, W., Jin, J., and Li, Y., 2009. Prediction of inflow at Three Gorges Dam in Yangtze River with wavelet network model. Water Resources Management, 23 (13), 2791–2803.
   Web of Science ®Google Scholar
• Wu, C.L., Chau, K.W., and Li, Y.S., 2009. Methods to improve neural network performance in daily flows prediction. Journal of Hydrology, 372, 80–93.
   Web of Science ®Google Scholar