Advances in ungauged streamflow prediction using artificial neural networks

Adamowski, 2008, 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, 10.1016/j.jhydrol.2008.02.013 Albers, 2000 Allen, 1995 Alp, 2007, Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data, Environmental Modeling & software, 22, 2, 10.1016/j.envsoft.2005.09.009 Arnell, N., et al., 2001. Climate change 2001: impacts, adaptation and vulnerability: hydrology and water resources. United Nations Environmental Program, Intergovernmental Panel on Climate Change. Aytek, 2008, An application of artificial intelligence for rainfall–runoff modeling, Journal of Earth System Science, 117, 145, 10.1007/s12040-008-0005-2 Besaw, 2007, Stochastic simulation and spatial estimation with multiple data types using artificial neural networks, Water Resources Research, 43, W11409, 10.1029/2006WR005509 Brooks, 2003 Chaloulakou, 1999, Forecasting daily maximum ozone concentration in the Athens basin, Environmental Monitoring and Assessment, 56 Chang, 2001, A counterpropagation fuzzy-neural network modeling approach to real time stream flow prediction, Journal of Hydrology, 245, 153, 10.1016/S0022-1694(01)00350-X Chang, 2001, Counterpropagation fuzzy-neural network for streamflow reconstruction, Hydrological Processes, 15, 219, 10.1002/hyp.102 Chang, 2002, Real-time recurrent learning neural network for stream-flow forecasting, Hydrological Processes, 16, 2577, 10.1002/hyp.1015 Chiew, 1994, Application of the daily rainfall runoff model MODHYDROLOG to 28 catchments, Journal of Hydrology, 153, 383, 10.1016/0022-1694(94)90200-3 Cigizoglu, 2003, Estimation, forecasting and extrapolation of river flows by artificial neural networks, Hydrological Sciences, 48, 349, 10.1623/hysj.48.3.349.45288 Cigizoglu, 2005, Application of generalized regression neural networks to intermittent flow forecasting and estimation, Journal of Hydrologic Engineering, 10, 336, 10.1061/(ASCE)1084-0699(2005)10:4(336) Cigizoglu, 2005, Generalized regression neural network in monthly flow forecasting, Civil Engineering and Environmental Systems, 22, 71, 10.1080/10286600500126256 Connor, 1994, Recurrent neural networks and robust time series prediction, IEEE Transactions on Neural Networks, 5, 240, 10.1109/72.279188 Doolan, 1996, The Geology of Vermont, Rocks and Minerals, 71, 218, 10.1080/00357529.1996.9924875 Emrah, 2007, Estimation of total sediment load concentration obtained by experimental study using artificial neural networks, Environmental fluid mechanics, 7, 271, 10.1007/s10652-007-9025-8 Firat, 2008, Comparison of artificial intelligence techniques for river flow forecasting, Hydrology and Earth System Sciences, 12, 123, 10.5194/hess-12-123-2008 Firat, 2008, Hydrological time-series modelling using an adaptive neuro-fuzzy inference system, Hydrological Processes, 22, 2122, 10.1002/hyp.6812 Geological_Survey, US, 2009. USGS Surface-Water Data for USA. Govindaraju, 2000, Artificial neural networks in hydrology II: hydrogeologic applications, Journal of Hydrologic Engineering, 5, 124, 10.1061/(ASCE)1084-0699(2000)5:2(124) Govindaraju, 2000 Hackett, W.R., 2009. Changing Land Use, Climate and Hydrology in the Winooski River Basin, Vermont. M.S. Thesis, Univ. of Vermont, Burlington. Hecht-Nielsen, 1987, Counterpropagation Networks, Applied Optics, 26, 4979, 10.1364/AO.26.004979 Hijmans, 2005, Very high resolution interpolated climate surface for global land areas, International Journal of Climatology, 25, 1965, 10.1002/joc.1276 Hsieh, 2003, Seasonal prediction with error estimation of Columbia river streamflow in British Columbia, Journal of Water Resources Planning and Management – ASCE, 129, 146, 10.1061/(ASCE)0733-9496(2003)129:2(146) Hsu, 1995, Artificial neural network modeling of the rainfall–runoff process, Water Resources Research, 31, 2517, 10.1029/95WR01955 Hsu, 2002, Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis, Water Resources Research, 38, 1312, 10.1029/2001WR000795 Jakeman, 1990, Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments, Journal of Hydrology, 117, 275, 10.1016/0022-1694(90)90097-H Khalil, 2005, Basin scale water management and forecasting using artificial neural networks, Journal of American Water Resources Association, 41, 195, 10.1111/j.1752-1688.2005.tb03728.x Kingston, 2005, Calibration and validation of neural networks to ensure physically plausible hydrological modeling, Journal of Hydrology, 314, 158, 10.1016/j.jhydrol.2005.03.013 Kisi, 2005, Daily river flow forecasting using artificial neural networks and auto-regressive models, Turkish Journal of Engineering and Environmental Sciences, 29, 9 Kisi, 2008, River flow forecasting and estimation using different artificial neural network techniques, Hydrology Research, 39, 27, 10.2166/nh.2008.026 Kokkonen, 2001, A comparison of metric and conceptual approaches in rainfall–runoff modeling and its implications, Water Resources Research, 37, 2345, 10.1029/2001WR000299 Krause, 2005, Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences, 5, 89, 10.5194/adgeo-5-89-2005 Leopold, 1964 Maier, 2000, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environmental Modelling & Software, 15, 101, 10.1016/S1364-8152(99)00007-9 McKerchar, 1974, Applications of seasonal parametric linear stochastic models to monthly flow data, Water Resources Research, 10, 246, 10.1029/WR010i002p00246 Mohamoud, 2008, Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves, Hydrological Sciences, 53, 706, 10.1623/hysj.53.4.706 Moradkhani, 2004, Improved streamflow forecasting using self-organizing radial basis function artificial neural networks, Journal of Hydrology, 295, 246, 10.1016/j.jhydrol.2004.03.027 Nash, 1970, River flow forecasting through conceptual models, Part I – a discussion of principles, Journal of Hydrology, 10, 282, 10.1016/0022-1694(70)90255-6 Phien, 1990, Daily forecasting with regression analysis, Water SA, 16, 179 Rajaee, 2009, Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models, Science of the total environment, 407, 4916, 10.1016/j.scitotenv.2009.05.016 Rajurkar, 2002, Artificial neural networks for daily rainfall–runoff modelling, Hydrological Sciences, 47, 865, 10.1080/02626660209492996 Rizzo, 1994, Characterization of aquifer properties using artificial neural networks: neural kriging, Water Resources Research, 30, 483, 10.1029/93WR02477 Schilling, 2005, Estimation of streamflow, baseflow and nitrate–nitrogen loads in Iowa using multiple regression models, Journal of American Water Resources Association, 41, 1333, 10.1111/j.1752-1688.2005.tb03803.x Singh, 2007, Suitability of different neural networks in daily flow forecasting, Applied Soft Computing, 7, 968, 10.1016/j.asoc.2006.05.003 Specht, 1991, A general regression neural network, IEEE Transactions on Neural Networks, 2, 10.1109/72.97934 Tangborn, 1976, Hydrology of the North Cascades region, Washington – Part 2: a proposed hydrometeorological streamflow prediction method, Water Resources Research, 12, 203, 10.1029/WR012i002p00203 VanderKwaak, 2001, Hydrologic-response simulations for the R-5 catchment with a comprehensive physics-based model, Water Resources Research, 37, 999, 10.1029/2000WR900272 Vianello, 2007, Bankfull width and morphological units in an alpine stream of dolomites (Northern Italy), Geomorphology, 83, 266, 10.1016/j.geomorph.2006.02.023 Walker, 2003, New technologies require advances in hydrologic data assimilation, EOS, 84, 545, 10.1029/2003EO490002 Wang, 2006, Forecasting daily streamflow using hybrid ANN models, Journal of Hydrology, 324, 383, 10.1016/j.jhydrol.2005.09.032 Wang, 2008, Storm-even rainfall–runoff modelling approach for ungauged sites in Taiwan, Hydrological Processes, 22, 4322, 10.1002/hyp.7019 Yang, M.-D., Chen, B.P.T., Chen, C.-S., 2007. Using artificial neural network for outflow estimation in an ungauged area. In: IEEE International Joint Conference on Neural Networks, pp. 3551–3555. Yurekli, 2005, Testing residuals of an ARIMA model on the Cekerek stream watershed in Turkey, Turkish Journal of Engineering and Environmental Sciences, 29, 61 Zealand, 1999, Short term streamflow forecasting using artificial neural networks, Journal of Hydrology, 214, 32, 10.1016/S0022-1694(98)00242-X