Real-time flow forecasting in the absence of quantitative precipitation forecasts: A multi-model approach

Journal of Hydrology - Tập 334 - Trang 125-140 - 2007
Monomoy Goswami1, Kieran M. O’Connor1
1Department of Engineering Hydrology, National University of Ireland, Galway, Ireland

Tài liệu tham khảo

Aspinall, 2004, Modelling land use change with generalized linear models–a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana, Journal of Environmental Management, 72, 91, 10.1016/j.jenvman.2004.02.009 Birikundavyi, 2002, Performance of neural network in daily streamflow forecasting, ASCE Journal of Hydrologic Engineering, 7, 393, 10.1061/(ASCE)1084-0699(2002)7:5(392) Box, 1976 Campolo, 1999, River flood forecasting with a neural network model, Water Resources Research, 35, 1191, 10.1029/1998WR900086 Dawson, 1998, An artificial neural network approach to rainfall–runoff modeling, Hydrological Sciences Journal, 43, 47, 10.1080/02626669809492102 Elshorbagy, 2000, Performance evaluation of artificial neural networks for runoff prediction, Journal of Hydrologic Engineering, 5, 424, 10.1061/(ASCE)1084-0699(2000)5:4(424) French, 1992, Rainfall forecasting in space and time using a neural network, Journal of Hydrology, 137, 1, 10.1016/0022-1694(92)90046-X Goswami, 2005, Real-time river flow forecasting for the Brosna catchment in Ireland using eight updating models, Hydrology and Earth System Sciences, 9, 394, 10.5194/hess-9-394-2005 Haan, 1977 Halff, 1993, Predicting runoff from rainfall using neural networks, 760 Hu, 2001, River flow time series prediction with a range-dependent neural network, Hydrological Sciences Journal, 46, 729, 10.1080/02626660109492867 Jain, 2003, Comparative analysis of event based rainfall–runoff modeling techniques-deterministic, statistical and artificial neural networks, Journal of Hydrologic Engineering, 8, 93, 10.1061/(ASCE)1084-0699(2003)8:2(93) Jain, 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, 10.1029/2003WR002355 Karunanithi, 1994, Neural networks for river flow prediction, Journal of Computing Civil Engineering, 8, 201, 10.1061/(ASCE)0887-3801(1994)8:2(201) Minns, 1996, Artificial neural networks as rainfall–runoff models, Hydrological Sciences Journal, 41, 399, 10.1080/02626669609491511 1979 Nash, 1970, River flow forecasting through conceptual models. Part 1. A discussion of principles, Journal of Hydrology, 10, 282, 10.1016/0022-1694(70)90255-6 Neldar, 1965, A simplex method for function minimization, Computer Journal, 7, 308, 10.1093/comjnl/7.4.308 O’Connor, 2006, The Galway real-time river flow forecasting system O’Connor, K.M., Goswami, M., Bhattarai, K.P., Shamseldin, A.Y., 2004. A comparison of the lead-time discharge forecasts of the ‘Perfect’ and ‘Naı¨ve-AR’ Quantitative Precipitation Forecast (QPF) input scenarios, to assess the value of having good QPFs. In: Brath, A., Montanari, A., Toth, E. (Eds.), ‘Hydrological Risk: Recent Advances in Peak River Flow Modelling, Prediction and Real-time Forecasting–Assessment of the Impacts of Land-use and Climate Changes. Editoriale Bios s.a.s., pp. 187–217. ISBN 88-7740-378-0. Press, 1989 Rajurkar, 2004, Modeling of the daily rainfall–runoff relationship with artificial neural network, Journal of Hydrology, 285, 96, 10.1016/j.jhydrol.2003.08.011 Salas, 1980 See, 2001, Multi-model data-fusion for hydrological forecasting, Computers & Geosciences, 27, 987, 10.1016/S0098-3004(00)00136-9 Shamseldin, 1997, Application of a neural network technique to rainfall–runoff modelling, Journal of Hydrology, 199, 272, 10.1016/S0022-1694(96)03330-6 Shamseldin, 1999, A real-time combination method for the outputs of different rainfall–runoff models, Hydrological Sciences Journal, 44, 895, 10.1080/02626669909492288 Shamseldin, 2001, A non-linear neural network technique for updating of river flow forecasts, Hydrology and Earth System Sciences, 5, 577, 10.5194/hess-5-577-2001 Shamseldin, 1997, Methods for combining the outputs of different rainfall–runoff models, Journal of Hydrology, 197, 203, 10.1016/S0022-1694(96)03259-3 Shen, H.W. (Ed.), 1976. Stochastic Approaches to Water Resources, vol. 1. H.W. Shen, P.O. Box 606, Fort Collins, CO 80521, USA. Sivakumar, 2002, River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches, Journal of Hydrology, 265, 225, 10.1016/S0022-1694(02)00112-9 Smith, 1995, Neural-network models of rainfall–runoff process, Journal of Water Research Planning and Management ASCE, 121, 499, 10.1061/(ASCE)0733-9496(1995)121:6(499) Spendley, 1962, Sequential application of simplex designs in optimization and evolutionary design, Technometrics, 4, 441, 10.2307/1266283 Takagi, 1985, Fuzzy identification of systems and its application to modelling and control, IEEE Transactions on Systems Man and Cybernetics, 15, 116, 10.1109/TSMC.1985.6313399 Tingsanchali, 2000, Application of tank, NAM, ARMA and neural network models to flood forecasting, Hydrological Processes, 14, 2437, 10.1002/1099-1085(20001015)14:14<2473::AID-HYP109>3.0.CO;2-J Tokar, 1999, Rainfall–runoff modeling using artificial neural networks, ASCE Journal of Hydrologic Engineering, 4, 232, 10.1061/(ASCE)1084-0699(1999)4:3(232) Toth, 2000, Comparison of short-term rainfall prediction models for real-time flood forecasting, Journal of Hydrology, 239, 132, 10.1016/S0022-1694(00)00344-9 Xiong, 2001, A non-linear combination of the forecasts of rainfall–runoff models by the first-order Takagi–Sugeno fuzzy system, Journal of Hydrology, 245, 196, 10.1016/S0022-1694(01)00349-3 Zealand, 1999, Short term streamflow forecasting using artificial neural networks, Journal of Hydrology, 214, 32, 10.1016/S0022-1694(98)00242-X Zhang, 2000, Prediction of watershed runoff using Bayesian concepts of modular neural networks, Water Resources Research, 36, 753, 10.1029/1999WR900264