Hydroinformatics References
Version 1.8
(Last updated 1 June 2005 - latest additions from version 1.7 in green)
Additions / corrections to: C.W.Dawson1@lboro.ac.uk
Abrahart, R.J. (1998) 'Neural networks and the problem of accumulated error: An embedded solution
that offers new opportunities for modelling and testing', Hydroinformatics, August, pp 725 - 731.
Abrahart, R.J. (1999) 'Neurohydrology: implementation options and research agenda', Area, Vol
31(2), pp 141 - 149.
Abrahart, R.J. and Kneale, P.E. (1997) 'Exploring Neural Network Rainfall-Runoff Modelling',
Proceedings of the 6th British Hydrological Society Symposium, Salford University, pp 9.35 9.44.
Abrahart, R.J. and See, L. (1998) 'Neural network vs. ARMA modelling: constructing benchmark
case studies of river flow prediction', Proceedings of the 3rd International Conference on
Geocomputation,
University
of
Bristol,
17-19
September,
http://www.geog.port.ac.uk/geocomp/geo98/05/gc_05.htm (23 February, 2000).
Abrahart, R.J. and See, L. (2000) 'Comparing neural network and autoregressive moving average
techniques for the provision of continuous river flow forecasts in two contrasting catchments’,
Hydrological Processes, Vol 14, pp 2157 - 2172.
Abrahart, R.J. See, L. and Kneale, P.E. (1998) ‘New tools for neurohydrologists: using network
pruning and model breeding algorithms to discover optimum inputs and architectures’,
Proceedings of the 3rd International Conference on Geocomputation, University of Bristol, 1719 September.
Abrahart, R.J. See, L. and Kneale, E. (1999) 'Applying saliency analysis to neural network rainfallrunoff modelling', Proceedings of the 4th International Conference on Geocomputation,
Fredericksburg, Virginia, USA, 25-28 July.
Abrahart, R.J. See, L. and Kneale, E. (1999) 'Using pruning algorithms and genetic algorithms to
optimise network architectures and forecasting inputs in a neural network rainfall-runoff model',
Journal of Hydroinformatics, Vol 1(2), pp 103 - 114.
Abrahart, R.J. and White, S.M. (2001) ‘Modelling sediment transfer in Malawi: Comparing
backpropagation neural network solutions against a multiple linear regression benchmark using
small data sets’, Physics and Chemistry of the Earth, Vol 26(1), pp 19 - 24.
Abrahart, R.J. (2003) ‘Neural network rainfall-runoff forecasting based on continuous resampling’,
Journal of Hydroinformatics, Vol 5(1), pp 51 – 61.
Ahsen, M. and O’Connor, K.M. (1994) ‘A Simple non-linear rainfall-runoff model with variable gain
factor’, Journal of Hydrology, Vol 155, pp 151 - 183.
Aitkenhead,M.J. McDonald, A.J.S. Dawson, J.J. Couper, G. Smart, R.P. Billett, M. Hope, D. and
Palmer, S. (2003) ‘A novel method for training neural networks for time-series prediction in
environmental systems’, Ecological Modelling, Vol 162, pp 87 – 95.
Alexander, D. (1991) ‘Information technology in real-time for monitoring and managing natural
disasters’, Progress in Physical Geography, Vol 15, pp 238 - 260.
Anctil, F. Michel, C. Perrin, C. and Andreassian, V. (2004) ‘A soil moisture index as an auxiliary
ANN input for stream flow forecasting’, Journal of Hydrology, Vol 286, pp 155 – 167.
Anderson, M.G. and Burt, T.P. (eds.) (1985) 'Hydrological Forecasting', Wiley, Chichester.
ANNEXG - Artificial Neural Network Experiment Group (2002) 'An International Comparative
Study of Artificial Neural Network Techniques for River Stage Forecasting', British
Hydrological Society 8th National Hydrology Symposium, Birmingham University, 8 - 11
September, ISBN 090374105X, pp 75-81.
ASCE (2000a) 'Artificial neural networks in hydrology.
I: Preliminary concepts', Journal of
Hydrologic Engineering, Vol 5(2), pp 115 - 123.
ASCE (2000b) 'Artificial neural networks in hydrology. II: Hydrologic applications', Journal of
Hydrologic Engineering, Vol 5(2), pp 124 - 137.
Atiya, A. El-Shoura, S. Shaheen, S. and El-Sherif, M. (1996) ‘River flow forecasting using neural
networks’, World Congress Neural Networks, San Diego, CA, September, pp 461 - 464.
Atiya, A. El-Shoura, S. Shaheen, S.I. and El-Sherif, M.S. (1997) ‘Application of neural networks to
the problem of forecasting the flow of the River Nile’, 7th IEEE Workshop on Neural Networks
for Signal Processing, NNSP ‘97, Amelia Island, Florida; Picscataway, NJ, USA, pp598 - 606.
Atiya, A. El-Shoura, S. Shaheen, S.I. and El-Sherif, M.S. (1999) ‘A comparison between neuralnetwork forecasting techniques - case study: river flow forecasting’, IEEE Transactions on
Neural Networks, Vol 10(2), pp 402 - 409.
Ball, J.E. (1994) ‘The influence of storm temporal patterns on catchment response’, Journal of
Hydrology, Vol 158(1-4), pp 285 - 303.
Baratti, R. Cannas, B. Fanni, A. Pintus, M. Sechi, G.M. and Toreno, N. (2003) ‘River flow forecast
for reservoir management for neural networks’, Neurocomputing, Vol 55, pp 421 – 437.
Bazartseren, B. Hildebrandt, G. and Holz, K.P. (2003) ‘Short-term water level prediction using
neural networks and neuro-fuzzy approach’, Neurocomputing, Vol 55, pp 439 – 450.
Bengtsson, L. and Malm, J. (1997) ‘Using rainfall-runoff modeling to interpret lake level data’,
Journal of Paleolimnology, Vol 18(3), pp 235 - 248.
Bodri, L. and Cermak, V. (2000) 'Prediction of extreme precipitation using a neural network:
application to summer flood occurance in Moravia', Advances in Engineering Software, Vol
31(5), pp 311 - 321.
Bonafe, A. Galeati, G. and Bitetto, G. (1997) ‘Daily mean flow forecasting by a neural network
model’, Proceedings XIX Congres des Grands Barrages, September 1997, Florence, Italy, pp 539
- 550.
Bowden, G.J. Dandy, G.C. and Maier, H.R. (2003) ‘Data transformation for neural network models
in water resources applications’, Journal of Hydroinformatics, Vol 5(4), pp 245 – 258.
Bowden, G.J. Maier, H.R. and Dandy, G.C. (2005) ‘Input determination for neural network models
in water resources applications. Part 1 – background and methodology’, Journal of Hydrology,
Vol 301, pp 75 – 92.
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, Vol 301, pp 93 – 107.
Braddock, R.D. Kremmer, M.L. and Sanzogni, L. (1998) 'Feed-forward artificial neural network
model for forecasting rainfall run-off', Environmetrics, Vol 9(4), pp 419 - 432.
Brath, A. Montanari, A. and Toth, E. (1999) 'Short-term rainfall prediction with time series analysis
techniques for real-time flash flood forecasting', Proceedings EGS Conference on Mediteranean
Storms, October, in press.
Buchtele, J. Elias, V. Tesar, M. and Herrman, A. (1996) ‘Runoff components simulated by rainfall
runoff models’, Hydrological Sciences Journal, Vol 41(1), pp 49 - 60.
Burlando, P. Rosso, R. Cadavid, L.G. and Salas, J.D. (1993) ‘Forecasting of short-term rainfall using
ARMA models’, Journal of Hydrology, Vol 144 (1-4), pp 193 - 211.
Cameron, D. Kneale, P. and See, L. (2002) 'An evaluation of a traditional and a neural net modelling
approach to flood forecasting for an upland catchment', Hydrological Processes, Vol 16, pp 1033
- 1046.
Campolo, M. Andreussi, P. and Soldati, A. (1999) 'River flood forecasting with a neural network
model', Water Resources Research, Vol 35(4), pp 1191 - 1197.
Campolo, M. Soldati, A. and Andreussi, P. (1999) 'Forecasting river flow rate during low-flow
periods using neural networks', Water Resources Research, Vol 35(11), pp 3547 - 3552.
Campolo, M. Soldati, A. and Andreussi, P. (2003) ‘Artificial neural network approach to flood
forecasting in the River Arno’, Hydrological Sciences Journal, Vol 48(3), pp 381 – 398.
Cannas, B. Carboni, A. Fanni, M. and Sechi, G.M. (2000) ‘Dynamic neural networks for the water
flow forecasting’, International Conference on Engineering Applications of Neural Networks, 17
– 19 July, Kingston University, UK, Tsaptsinos, D. (Ed.).
Cannas, B. Fanni, A. Pintus, M. and Sechi, G.M. (2001) ‘Alternative neural network models for the
rainfall-runoff process’, International Conference on Engineering Applications of Neural
Networks, 16 – 18 July, University of Cagliari, Italy; Evolving Solution with Neural Networks,
Baratatti, R. and Fernandez de Canete, J. (Eds.).
Cannon, A.J. and Whitfield, P.H. (2002) 'Downscaling recent streamflow conditions in British
Columbia, Canada using ensemble neural network models', Journal of Hydrology, Vol 259(1-4),
pp 136-151.
Chang, F.J. and Chen, Y.C. (2003) ‘Estuary water-stage forecasting by using radial basis function
neural network’, Journal of Hydrology, Vol 270, pp 158 – 166.
Chang, F.J. Chang, L.C. and Huang, H.L. (2002) 'Real-time recurrent learning neural network for
stream-flow forecasting', Hydrological Processes, Vol 16, pp 2577 - 2588.
Chang, F. and Hwang, Y. (1999) 'A self-organization algorithm for real-time flood forecast',
Hydrological Processes, Vol 13, pp 123 - 138.
Chang, F.J. Hu, H.F. and Chen, Y.C. (2001) ‘Counterpropagation fuzzy-neural network for
streamflow reconstruction’, Hydrological Processes, Vol 15(2), pp 219 - 232.
Cheng, X. and Noguchi, M. (1996) 'Rainfall-runoff modelling by neural network approach', Proc. Int.
Conf. on Water Resour. & Environ. Res., Vol 2, pp 143 - 150.
Cheng, C.T. Ou, C.P. and Chau, K.W. (2002) ‘Combining a fuzzy optimal model with a genetic
algorithm to solve multi-objective rainfall-runoff model calibration’, Journal of Hydrology, Vol
268, pp 72 – 86.
Chiang, Y.M. Chang, L.C. and Chang, F.J. (2004) ‘Comparison of static-feedforward and dynamicfeedback neural networks for rainfall-runoff modelling’, Journal of Hydrology, Vol 290, pp 297311.
Chibanga, R. Berlamont, J. and Vandewalle, J. (2003) ‘Modelling and forecasting of hydrological
variables using artificial neural networks: the Kafue River sub-basin’, Hydrological Sciences
Journal, Vol 48(3), pp 363 – 379.
Chiew, F.H.S. and McMahon, T.A. (1991) ‘The applicability of Morton’s and Penman’s
evapotranspiration estimates in rainfall-runoff modeling’, Water Resources Bulletin, Vol 27(4),
pp 611 - 620.
Chiew, F.H.S. Stewardson, M.J. and McMahon, T.A. (1993) ‘Comparison of six rainfall-runoff
modelling approaches’, Journal of Hydrology, Vol 147, pp 1 - 36.
Chow, T.W.S. and Cho, S.Y. (1997) ‘Development of a recurrent sigma-pi neural network rainfall
forecasting system in Hong Kong’, Neural Computing and Applications, Vol 5(2), pp 66 - 75.
Cigizoglu, H.K. (2003) ‘Estimation, forecasting and extrapolation of river flows by artificial neural
networks’, Hydrological Sciences Journal, Vol 48(3), pp 349 – 361.
Cigizoglu, H.K. and Alp, M. (2004) ‘Rainfall-runoff modelling using three neural network methods’,
Rutkowski, L. et al (Eds), ICAISC, LNAI 3070, Springer-Verlag, Berlin, pp 166 – 171.
Clair, T.A. and Ehrman, J.M. (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, Vol 41(5), pp 921 - 927.
Coulibaly, P. Anctil, F. and Bobée, B. (1999) 'Hydrological Forecasting with artificial neural
networks: the state of the art', Canadian Journal of Civil Engineering, Vol 26(3), pp 293 - 304 (in
French).
Coulibaly, P. Anctil, F. and Bobée, B. (2000) 'Daily resevoir inflow forecasting using artificial neural
networks with stopped training approach', Journal of Hydrology, Vol 230(3-4), pp 244 - 257.
Coulibaly, P. Anctil, F. Rasmussen, P. and Bobée, B. (2000) ‘A recurrent neural networks approach
using indices of low-frequency climatic variability to forecast regional annual runoff’,
Hydrological Processes, Vol 14, pp 2755 - 2777.
Coulibaly, P. Anctil, F. Aravena, R. Bobée, B. (2001) ‘Artificial neural network modeling of water
table depth fluctuation’, Water Resources Research, Vol. 37(4), pp 885 - 896.
Coulibaly, P. Bobée, B. and Anctil, F. (2001) ‘Improving extreme hydrologic events forecasting
using a new criterion for artificial neural network selection’, Hydrological Processes, Vol 15(8),
pp 1533 - 1536.
Crespo, J.L. and Mora, E. (1993) 'Drought estimation with neural networks', Advances in
Engineering Software, Vol 18(3), pp 167 - 170.
Daliakopoulos, I.N. Coulibaly, P. and Tsanis, I.K. (2005) ‘Groundwater level forecasting using
artificial neural networks’, Journal of Hydrology, Vol 309, pp 229 – 240.
Dandy, G. and Maier, H. (1996) ‘Use of Artificial Neural Networks for Real Time Forecasting of
Water Quality’, Proc of the International Conference on Water Resources and Environmental
Research, Vol 2, Japan, 55 - 64.
Danh, N.T. Phien, H.N. and Gupta, A.D. (1999) 'Neural networks for river flow forecasting', Water
SA, Vol 25(1), pp 33 - 39.
Daniell, T.M . (1991) 'Neural networks - applications in hydrology and water resources engineering'
[water consumption (Canberra) and regional flood estimation (ACT)],
hydrology and water resources symposium, Perth, WA, Vol 3.
[Sydney], Vol 91(22), pp 797 - 802.
In: International
Natl Conf Publ Inst Eng Aust
Dastorani, M.T. and Wright, N. (2001) ‘Application of Artificial Neural Networks for Ungauged
Catchments Flood Prediction’, Presented at Floodplain Management Association conference,
San Diego, CA, March 2001.
Dawson, C.W. and Wilby, R. (1998) 'An Artificial Neural Network Approach To Rainfall-Runoff
Modelling', Hydrological Sciences Journal, Vol 43(1), pp 47 - 66.
Dawson, C.W. and Wilby, R. (1999) 'A comparison of artificial neural networks used for river flow
forecasting', Hydrology and Earth System Sciences, Vol 3(4), pp 529 - 540.
Dawson, C.W. Brown, M. and Wilby, R. (2000) 'Inductive learning approaches to rainfall-runoff
modelling', International Journal of Neural Systems, Vol 10(1), pp 43 - 57.
Dawson, C.W. and Wilby, R.L. (2001) 'Hydrological modelling using artificial neural networks',
Progress in Physical Geography, Vol 25(1), pp 80 - 108.
Dawson, C.W. Harpham, C. Wilby, R.L. and Chen, Y. (2002) 'An Evaluation of Artificial Neural
Network Techniques for Flow Forecasting in the River Yangtze, China', Hydrology and Earth
System Sciences, Vol 6(4), pp 619-626.
Dekker, S.C. Bouten, W. and Schaap, M.G. (2001) ‘Analysing forest transpiration model errors with
artificial neural networks’, Journal of Hydrology, Vol 246, pp 197 - 208.
Deo, M.C. and Thirumalaiah, K. (2000) 'Real time forecasting using neural networks', in
Govindaraju, R.S. and Ramachandra Rao, A. (Eds) 'Artificial neural networks in hydrology',
Kluwer Academic, Dordrecht, pp 53 - 71.
Dibike, Y.B. and Solomatine, D.P. (2001) 'River flow forecasting using artificial neural networks',
Physics and Chemistry of the Earth Part B - Hydrology Oceans and Atmosphere, 1464-1909,
Vol 26(1), pp 1 - 7.
Dibike, Y.B. Solomatine, D. and Abbott, M.B. (1999) ‘On the encapsulation of numerical-hydraulic
models in artificial neural network’, Journal of Hydraulic Research, Vol 37(2), pp 147 - 161.
Dibike Y.B. (2000) ‘Machine learning paradigms for rainfall runoff modelling’, Proceedings of the
Hydroinformatics-2000 Conference, Iowa, U SA.
Dimopoulos, I. Lek, S. and Lauga, J. (1996) ‘Rainfall-runoff modelling by neural network and
Kalman Filter’, Hydrological Sciences Journal, Vol 41(2), pp 179 - 193 (French).
Diskin, M.H. and Simon, E. (1977) ‘A Procedure for The Selection of Objective Functions for
Hydrological Conceptual Models’, Journal of Hydrology, Vol 34, 129 - 149.
Dobson, C. and Davies, G.P. (1990) ‘Integrated real time data retrieval and flood forecasting using
conceptual models’, International Conference on River Hydraulics, 17-20 September, pp 469 477.
Dolling, O.R. and Varas, E.A. (2002) 'Artificial neural networks for streamflow prediction', Journal
of Hydraulic Research, Vol 40, pp 547 - 554.
Douglas, J.R. and Dobson, C. (1987) ‘Real time flood forecasting in diverse drainage basins’, In
Collinge, V.K. and Kirkby, C. (Eds.) Weather radar and flood forecasting, pp 153-169, Wiley,
London.
Drogue, G. et al. (2002) ‘The applicability of a parimonious model for local and regional prediction
of runoff’, Hydrological Sciences Journal, Vol 47(6), pp 905 - 920.
Elkamel, A. (1998) 'An artificial neural network for predicting and optimizing immiscible flood
performance in heterogeneous reservoirs', Computers and Chemical Engineering, Vol 22(11), pp
1699 - 1709.
Elshorbagy, A.A. Panu, U.S. and Simonovic, S.P. (2000) 'Group-based estimation of missing
hydrological data: I. Approach and general methodology', Hydrological Sciences Journal, Vol
45(6), pp 849 - 866.
Elshorbagy, A. Simonovic, S. P. and Panu, U. S. (2000) ‘Performance Evaluation of Artificial Neural
Networks for Runoff Prediction’, Journal of Hydrologic Engineering, Vol 5(4), pp 424 - 427.
Elshorbagy, A.A. Panu, U.S. and Simonovic, S.P. (2001) 'Analysis of cross-correlated chaotic
streamflows', Hydrological Sciences Journal, Vol 46(5), pp 781-793.
Faures, J. Goodrich, D.C. Woolhiser, D.A. and Sorooshian, S. (1995) ‘Impact of small-scale spatial
rainfall variability on runoff modeling’, Journal of Hydrology, Vol 173, pp 309 - 326.
Fernando, D.A.K. and Jayawardena, A.W. (1998) 'Runoff forecasting using RBF networks with OLS
algorithm', Journal of Hydrologic Engineering, Vol 3(3), pp 203 - 209.
Fontaine, T.A. (1995) ‘Rainfall-runoff model accuracy for an extreme flood’, Journal of Hydraulic
Engineering Association, Vol 121(4), pp 365 - 374.
Fortin, V. Ouarda, T.B.M.J. and Bobée, B. (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, Vol 33, pp 2423 - 2424.
Franchini, M. (1996) ‘Use of a genetic algorithm combined with a local search method for the
automatic calibration of conceptual rainfall-runoff models’, Hydrological Sciences Journal, Vol
41(1), pp 21 - 39.
Franchini, M. and Galeati, G. (1997) ‘Comparing several genetic algorithm schemes for the
calibration of conceptual rainfall-runoff models’, Hydrological Sciences Journal, Vol 42(3), pp
357 - ??.
Franchini, M. and Pacciani, M. (1991) ‘Comparative analysis of several conceptual rainfall-runoff
models’, Journal of Hydrology, Vol 122(1-4), pp 161 - 219.
Franchini, M. Galeati, G. and Berra, S. (1998) ‘Global optimization techniques for the calibration of
conceptual rainfall-runoff models’, Hydrological Sciences Journal, Vol 43(3), pp 443 - 458.
Franchini, M. Helmlinger, K.R. Foufoula-Georgiou, E. and Todini, E. (1996) ‘ Stochastic storm
transposition coupled with rainfall runoff modeling for estimation of exceedance probabilities of
design floods’, Journal of Hydrology, Vol 175(1-4), pp 511-532.
French, M.N. Krajewski, W.F. and Cuykendall, R.R. (1992) ‘Rainfall Forecasting in Space and Time
Using a Neural Network’, Journal of Hydrology, Vol 137, pp 1-31.
Furundzic, D. (1998) ‘Application of neural networks for time series analysis: rainfall-runoff
modeling’, Signal Processing, Vol 64(3), pp 383 - 396.
Gan, T.Y. and Biftu, G.F. (1996) ‘Automatic calibration of conceptual rainfall-runoff models:
optimization algorithms, catchment conditions, and model structure’, Water Resources Research,
Vol 32(12), pp 3513 - 3524.
Garfias, J. et al (1996) ‘Choice of a rainfall-runoff model for complex hydrological conditions’,
Journal of Hydrology, Vol 176, pp 227 - 247 (French).
Garrote, L. and Bras, R.L. (1995) ‘A distributed model for real-time flood forecasting using digital
elevation models’, Journal of Hydrology, Vol 167, pp 279 - 306.
Gaume, E. and Gosset, R. (2003) ‘Over-parameterisation, a major obstacle to the use of artificial
neural networks in hydrology?’, Hydrology and Earth Systems Sciences, Vol 7(5), pp 693 – 706.
Gautam, M.R. Watanabe, K. and Saegusa, H. (2000) ‘Runoff analysis in humid forest catchment
with artificial neural network’, Journal of Hydrology, Vol 235, pp 117 - 136.
Georgakakos, K.P. and Foufoula-Georgiou, E. (1991) ‘Real time coupling of hydrologic and
meteorological models for flood forecasting’, In Bowles, D.S. and O'Connell, P.E. (Eds.)
Proceedings of the NATO Advanced Study Institute on Recent Advances in the Modelling of
Hydrologic Systems. Sintra, Portugal, July 10-23, 1988, Kluwer Academic Publishers.
Giustolisi, O. (2000) 'Input-output dynamic neural networks simulating inflow-outflow phenomena
in an urban hydrological basin', Journal of Hydroinformatics, Vol 2(4), pp 269 - 279.
Giustolisi, O. and Laucelli, D. (2005) ‘Improving generalisation of artificial neural networks in
rainfall-runoff modelling’, Hydrological Sciences Journal, Vol 50(3), pp 439 – 457.
Golob, R. Stokelj, T. and Grgic, D. (1998) 'Neural-network-based water inflow forecasting', Control
Engineering Practice, Vol 6(5), pp 593 - 600.
Goodrich, D.C. Faures, J. Woolhiser, D.A. Lane, L.J. and Sorooshian, S. (1995) ‘Measurement and
analysis of small scale convective storm rainfall variability’, Journal of Hydrology, Vol 173, pp
283 - 308.
Goswami, P. (1997) ‘An experimental annual forecast of all-India mean summer monsoon rainfall
using a neural network’, Current Science, Vol 70(12), p 1039.
Goswami, P. and Kumar, P. (1997) ‘Experimental annual forecast of all-India mean summer
monsoon rainfall for 1997 using a neural network model’, Current Science, Vol 72(11), pp 781 782.
Goswami, P. and Srividya (1996) ‘A novel neural network design for long range prediction of
rainfall pattern’, Current Science, Vol 70(6), pp 447 - 457.
Govindaraju, R.S. and Ramachandra Rao, A. (Eds) (2000) 'Artificial neural networks in hydrology',
Kluwer Academic, Dordrecht, http://www.wkap.nl/book.htm/0-7923-6226-8 (3 May 2000).
Hall, M.J. and Minns, A.W. (1993) 'Rainfall-Runoff Modelling as a Problem in Artificial
Intelligence: Experience with a Neural Network', Proceedings of the 4th British Hydrological
Society Symposium, Cardiff, pp 5.51 - 5.57.
Hall, M.J. and Minns, A.W. (1999) ‘The classification of hydrologically homogeneous regions’,
Hydrological Sciences Journal, Vol 44(5), pp 693 - 704.
Hall, M.J. and Minns, A.W. (????) ‘Regional flood frequency analysis using artificial neural
networks’, unknown?
Hall, M.J. Minns, A.W. and Ashrafuzzaman, A.K.M. (????) ‘Regional flood frequency analysis
using artificial neural networks: a case study’, unknown?
Hall, M.J. Minns, A.W. and Ashrafuzzaman, A.K.M. (2000) ‘Regionalisation and data mining in a
data scarce environment’, BHS 7th National Hydrology Symposium, Newcastle.
Hall, M.J. Minns, A.W. and Ashrafuzzaman, A.K.M. (2002) ‘The application of data mining
techniques for the regionalisation of hydrological variables’, Hydrological and Earth System
Sciences, Vol 6(4), pp 685 – 694.
Han, D. Cluckie, I.D. Wedgwood, O. and Pearse, I. (1997) ‘The North West flood forecasting system
WRIP North West’, BHS 6th National Hydrology Symposium, Salford, pp 1.23 - 1.28.
Heggen, R. (1995) ‘Neural networks for river flow prediction’, Journal of Computing in Civil
Engineering, Vol 9(4), p 293. (discussion of Karunanithi et al 1994).
Hewitson, B.C. and Crane, R.G. (1994) 'Neural Nets: Applications in Geography', Kluwer Academic,
Dordrecht.
Hjelmfelt, A.T. and Wang, M. (1993) 'Runoff simulation using artificial neural networks',
Proceedings 25th Congress of the International Association for Hydraulic Research, Special
Lectures, Technical Session A, Flood and Drought, Vol 1, Tokyo, Japan, 30 August - 3
September, pp 517 - 522.
Hsieh, W.W. and Tang, B. (1998) ‘Applying neural network models to prediction and data analysis
in meteorology and oceanography’, Bulletin of the American Meteorological Society, Vol 79(9),
pp 1855 - 1870.
Hsu, K. Gupta, H.V. and Sorooshian, S. (1995) ‘Artificial Neural Network Modeling of the RainfallRunoff Process’, Water Resources Research, Vol 31(10), 2517 - 2530.
Hsu, K. Gupta, H.V. and Sorooshian, S. (1998) ‘Streamflow forecasting using artificial neural
networks’, Water Resources Engineering 98, Proceedings ASCE Conference, Memphis,
Tennessee.
Hsu, K. Gupta, H.V. Gao, X.G. and Sorooshian, S. (1999) ‘Estimation of physical variables from
multichannel remotely sensed imagery using a neural network: Application to rainfall
estimation’, Water Resources Research, Vol 35(5), pp 1605 - 1618.
Hsu, S. Masters, T. Kuhl, F.P. Tenorio, M.F. Reeves, A. and Grogan, T. (1991) ‘Comparative
analysis of five neural network models’, Technical Papers of the ACSM, Vol 5, pp 182 - 191.
Hsu, K.L. Gupta, H.V. Gao, X. Sorooshian, S. and Imam, B. (2002) ‘Self-organizing linear output
map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis’, Water
Resources Research, Vol 38(12), pp 1 – 17.
Hu, T.S. Lam. K.C. and Ng, S.T. (2001) ‘River flow time series prediction with a range-dependent
neural network’, Hydrological Sciences Journal, Vol 46(5), pp 729 - 245.
Hu, T. Yuan, P. and Di, J. (1995) 'Applications of artificial neural network to hydrology and water
resources ', Advances in Water Science [Nanjing], Vol 6(1), pp 76 - 82.
Hu, T. Wan, Y. and Feng, S. (1995) 'Research on the artificial neural network methodology for
multi-reservoir operating rules', Advances in Water Science [Nanjing], Vol 6(1), pp 53 - 60.
Ichiyanagi, K. Goto, Y. Mizuno, K. Yokomizu, Y. and Matsamura, T. (1995) 'An artificial neural
network to predict river flow rate into a dam for a hydro power plan', IEEE International
Conference on Neural Networks Proceedings, Perth, Australia, 27 Nov - 1 Dec, Vol 5, pp 2679 2682.
Ichiyanagi, K. Goto, Y. Yokomizu, Y. Matsamura, T. and Kito, Y. (1994) 'A prediction of river flow
rate into a dam for a hydro power plant by artificial neural network trained with data classified
according to total amount of rain', Proceedings of the IASTED International Conference Power
Systems and Engineering, 12-16 September, Wakayama, Japan. Hasegawa, J. (ed), pp 15 - 18.
Imrie, C.E. Durucan, S. and Korre, A. (2000) 'River flow prediction using artificial neural networks:
generalisation beyond the calibration range', Journal of Hydrology, Vol 233(1-4), pp 138 - 153.
Islam, S. and Kothari, R. (2000) 'Artificial neural networks in remote sensing of hydrological
processes', Journal of Hydrologic Engineering, Vol 5(2), pp 138 - 144.
Jain, S.K. (1993) ‘Calibration of conceptual models for rainfall-runoff simulation’, Hydrological
Sciences Journal, Vol 38(5), pp 431 - 441.
Jain, S.K. Das, A. and Srivastava, D.K. (1999) ‘Application of ANN for reservoir inflow prediction
and operation’, Journal of Water Resources Planning and Managment, Vol 125(5), pp 263 - 271.
Jain, S.K. and D. Chalisgaonkar, D. (2000), ‘Setting Up Stage-Discharge Relations Using ANN’,
Journal of Hydrologic Engineering, Vol 5(4), pp 428 - 433.
Jain, A. Sudheer, K.P. and Srinivasulu, S. (2004) ‘Identification of physical processes inherent in
artificial neural network rainfall runoff models’, Hydrological Processes, Vol 18, pp 571 – 581.
Jakeman, A.J. and Hornberger, G.M. (1993) ‘How much complexity is warranted in a rainfall-runoff
model?’, Water Resources Research, Vol 29(8), pp 2637 - 2649.
Jakeman, A.J. Post, D.A. and Beck, M.B. (1994) ‘From data and theory to environmental model: the
case of rainfall runoff’, ENVIRONMETRICS, Vol 5(3), pp 297 - 314.
Jayawardena, A.W. and Fernando, D.A.K. (1998) 'Use of Radial Basis Function Type Artificial
Neural Networks for Runoff simulation', Computer-aided Civil and Infrastructure Engineering,
Vol 13(2), pp 91 - 99.
Jayawardena, A.W. Fernando, D.A.K. and Zhou, M.C. (1997) ‘Comparison of Multilayer Perceptron
and Radial Basis Function networks as tools for flood forecasting’, Destructive Water: WaterCaused Natural Disaster, their Abatement and Control (Proceedings of the Conference at
Anaheim, CA, June), IAHS Publication Number 239, pp 173 - 181.
Jin, C.X. (1993) ‘Determination of basin lag times in rainfall-runoff investigations’, Hydrological
Processes, Vol 7(4), pp 449 - 457.
Kang, K.W. Park, C.Y. and Kim, J.H. (1993) 'Neural network and its application to rainfall-runoff
forecasting', Korean Journal of Hydroscience, Vol 4, pp 1 - 9.
Kang, K.W. Kim, J.H. Park, C.Y. and Ham, K.J. (1993) 'Evaluation of hydrologic forecasting system
based on neural network model', Proceedings 25th Congress of the International Association for
Hydraulic Research, Special Lectures, Technical Session A, Flood and Drought, Vol 1, Tokyo,
Japan, 30 August - 3 September, pp 257 - 264.
Karunanithi, N. Grenney, W.J. Whitley, D. and Bovee, K. (1994) ‘Neural Networks for River Flow
Prediction’, Journal of Computing in Civil Engineering, Vol 8(12), 201 - 220.
Khalil, M. Panu, U.S. and Lennox, W.C. (2001) ‘Groups and neural networks based streamflow data
infilling procedures’, Journal of Hydrology, Vol 241, pp 153 - 176.
Kim, G. and Barros, A.P. (2001) ‘Quantitative flood forecasting using multisensor data and neural
networks’, Journal of Hydrology, Vol 246, pp 45 - 62.
Kuchment, L.S. Demidov, V.N. Naden, P.S. Cooper, D.M. and Broadhurst, P. (1996) ‘Rainfallrunoff modelling of the Ouse basin, North Yorkshire: an application of a physically based
distributed model’, Journal of Hydrology, Vol 181(1-4), pp 323 - 342.
Kuligowski, R.J. and Barros, A.P. (1998) ‘Experiments in short-term precipitation forecasting using
artificial neural networks’, Monthly Weather Review, Vol 126, pp 470 - 482.
Kumar, A. and Minocha, V.K. (2001) ‘Rainfall Runoff Modeling Using Artificial Neural Networks’,
Journal of Hydrologic Engineering, Vol 6(2), pp 176 - 177.
Kumar, D.N. Raju, K.S. and Sathish, T. (2004) ‘River flow forecasting using recurrent neural
networks’, Water Resources Management, Vol 18, pp 143 – 161.
Lachtermacher, G. and Fuller, J.D. (1994) ‘Backpropogation in hydrological time series forecasting’,
in Stochastic and Statistical Methods in Hydrology and Environmental Engineering, Hipel, K.W.
et al (eds), Vol 3, pp 229 - 242.
Lamberti, P. and Pilati, S. (1996) ‘Flood propagation models for real-time forecasting’, Journal of
Hydrology, Vol 175(1-4), 239-266.
Lange, N.T. (1999) 'New mathematical approaches in hydrological modeling - an application of
artificial neural networks', Physics and Chemistry of the Earth, Vol 24(1-2), pp 31 - 35.
Lauzon, N. Rousselle, J. Birkundavyi, S. and Trung, H.T. (2000) ‘Real-time daily flow forecasting
using black-box models, diffusion processes, and neural networks’, Canadian Journal of Civil
Engineering, Vol 27(4), pp 671 - 682.
Legates, D.R. and McCabe, G.J. (1999) 'Evaluating the use of "goodness-of-fit" measures in
hydrologic and hydroclimatic model validation', Water Resources Research, Vol 35, pp 233 241.
Lek, S. Delacoste, M. Baran, P. Dimopoulos, I. Lauga, J. and Aulagnier, S. (1996) ‘Application of
neural networks to modelling nonlinear relationships in ecology’, Ecological Modelling, Vol
90(1), pp 39 - 52.
Lek, S. Dimopoulos, I. Derraz, M. and El-Ghachtoul, Y. (1996) ‘Rainfall-runoff modelling using
artificial neural networks’, Revue des Sciences de L’Eau, Vol 9(3), pp 319 - 331 (French).
Lekkas, D.F. Imrie, C.E. and Lees, M.J. (2001) 'Improved non-linear transfer function and neural
network methods of flow routing for real-time forecasting', Journal of Hydroinformatics, Vol
3(3), pp 153 - 164.
Liang, G.C. Kachroo, R.K. Kang, W. and Yu, X.Z. (1992) ‘River flow forecasting.
Part 4.
Applications of linear modelling techniques for flow routing on large catchments’, Journal of
Hydrology, Vol 133(1-4), pp 99 - 140.
Lin, G.F. and Chen, L.H. (2004) ‘A non-linear rainfall-runoff model using radial basis function
network’, Journal of Hydrology, Vol 289, pp 1-8.
Lingireddy, S. (1998) 'Aquifer-parameter estimation using genetic algorithms and neural networks',
Civil Engineering and Environmental Systems, Vol 15(2), pp 125 - 144.
Liong, S.Y. and Chan, W.T. (1993) 'Runoff volume estimates with neural networks', Proceedings of
3rd International Conference in Application of AI to Civil and Structural Engineering, Topping,
B.H.V and Khan, A.I. (eds), Civil Computer Press, UK, pp 67 - 70.
Liong, S. Y. Nguyen, V.T.V. Chan, W.T. and Chia, Y.S. (1994) 'Regional Estimation of Floods for
Ungaged Catchments with Neural Networks' In: Cheong, H-F. Shankar, N.J. Chan, E-S. and Ng,
W-J. eds., Developments in Hydraulic Engineering and their impact on the Environment,
Proceedings 9th Congress of the Asian and Pacific Division of the International Association for
Hydraulic Research, Singapore, 24 - 26 August, pp 372 - 378.
Liong, S.Y. Lim, W.H. Kojiri, T. and Hori, T. (2000) ‘Advance flood forecasting for flood stricken
Bangladesh with fuzzy reasoning method’, Hydrological Processes, Vol 14, pp 431 - 448.
Liong, S.Y. Khu, S.T. and Chan, W.T. (2001) ‘Derivation of Pareto Front with Genetic Algorithm
and Neural Network’, Journal of Hydrologic Engineering, Vol 6(1), pp 52 - 61.
Lischeid, G. and Uhlenbrook, S. (2003) ‘Checking a process-based catchment model by artificial
neural networks’, Hydrological Processes, Vol 17(2), pp 265 – 277.
Loke, E. Warnaars, E.A. Jacobsen, P. Nelen, F. and Almeida, M.D. (1997) 'Artificial neural networks
as a tool in urban storm drainage', Water Science and Technology, Vol 36(8-9), pp 101 - 109.
Lorrai, M. and Sechi, G.M. (1995) ‘Neural nets for modelling rainfall-runoff transformations’, Water
Resources Management, Vol 9(4), pp 299 - 213.
Luchetta, A. and Manetti, S. (2003) ‘A real time hydrological forecasting system using a fuzzy
clustering approach.’, Computers & Geosciences, Vol 29, pp 1111 – 1117.
Luk, K.C. Ball, J.E. and Sharma, A. (2000) 'A study of optimal lag and spatial inputs to artificial
neural networks for rainfall forecasting', Journal of Hydrology, Vol 227 (1-4), pp 56 - 65.
Maier, H.R. and Dandy, G.C. (1996a) 'The use of artificial neural networks for the prediction of
water quality parameters', Water Resources Research, Vol 32(4), pp 1013 - 1022.
Maier, H.R. and Dandy, G.C. (1996b) 'Neural network models for forecasting multivariate time
series', Neural Network World, Vol 6(5), pp 747 - 771.
Maier, H.R. and Dandy, G.C. (1997) 'Reply', Water Resources Research, Vol 33(10), pp 2425 - 2427.
Maier, H.R. and Dandy, G.C. (1998) 'The effect of internal parameters and geometry on the
performance of back-propagation neural networks: an empirical study', Environmental
Modelling & Software, Vol 13, pp 193 - 209.
Maier, H.R. Dandy, G.C. and Burch, M.D. (1998) 'Use of artificial neural networks for modelling
cyanobacteria Anabaena spp. in the River Murray, South Australia', Ecological Modelling, Vol
105(2/3), pp 257 - 272.
Maier H.R. and Dandy G.C. (1999) ‘Empirical comparison of various methods for training feedforward neural networks for salinity forecasting’, Water Resources Research, Vol 35(8), pp 2591
- 2596.
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
and Software, Vol 15(1), pp 101 - 123.
Mason, J.C. Tem’me, A. and Price, R.K. (1996) ‘A Neural Network Model of Rainfall-Runoff Using
Radial Basis Functions’, Journal of Hydraulic Research, Vol 34(4), 537 - 548.
Minns, A.W. (1995) ‘Analysis of experimental data using artificial neural networks’, Hydra 2000,
Proceedings XXVI Congress IAHR, Vol 1, pp 218 - 223.
Minns, A.W. (1996) 'Extended rainfall-runoff modelling using artificial neural networks', in Muller,
A. (ed), Hydroinformatics 96, Proceedings 2nd International Conference on Hydroinformatics,
Zurich, Vol 1, pp 207 - 213.
Minns, A.W. and Hall, M.J. (1996) 'Artificial Neural Networks as Rainfall-Runoff Models',
Hydrological Sciences Journal, Vol 41(3), pp 399 - 417.
Minns, A.W. and Hall, M.J. (1997) ‘Living with the Ultimate Black Box: More on Artificial Neural
Networks’, 6th National Hydrology Symposium, Salford University, 9.45 - 9.49.
Mizumura, K. and Chiu, C.L. (1985) ‘Prediction of combined snowmelt and rainfall runoff’, Journal
of Hydraulic Engineering - ASCE, Vol 111(2), pp 179 - 193.
Moradkhani, H. Hsu, K.L. Gupta, H.V. and Sorooshian, S. (2004) ‘Improved streamflow forecasting
using self-organizing radial basis function artificial neural networks’, Journal of Hydrology, Vol
295, pp 246 – 262.
Muleta, M.K. and Nicklow, J.W. (2004) ‘Joint application of artificial neural networks and
evolutionary algorithms to watershed management’, Water Resources Management, Vol 18, pp
459 – 482.
Muttiah, R.S. Srinivasan, R. and Allen, P.M. (1997) ‘Prediction of Two-Year Peak Stream
Discharges Using Neural Networks’, Journal of the American Water Resources Association, Vol
33(3), pp 625 - 630.
Naef, F. (1981) ‘Can we model the rainfall-runoff process today?’, Hydrological Sciences Bulletin,
Vol 26(3), pp 281 - 289.
Nalbantis, I. (1995) ‘Use of multiple time step information in rainfall runoff modelling’, Journal of
Hydrology, Vol 165, pp 135 - 159.
Nash, J.E. and Sutcliffe, J.V. (1970) 'River flow forecasting through conceptual models part 1 - a
discussion of principles', Journal of Hydrology, Vol 10, pp 282 - 290.
Olden, J.D. and Jackson, D.A. (2002) ‘Illuminating the ''black box'': a randomization approach for
understanding variable contributions in artificial neural networks’, Ecological Modelling, Vol
154 (1-2), pp 135 - 150.
O'Loughlin, G. Huber, W. and Chocat, B. (1996) 'Rainfall-runoff processes and modelling', Journal
of Hydraulic Research, Vol 34(6), pp 733 - 751.
Pan, T. and Wang, R. (2004) ‘State space neural networks for short term rainfall-runoff forecasting’,
Journal of Hydrology, Vol 297 (1-4), pp 34 – 50.
Pankiewicz, G.S. (1997) 'Neural network classification of convective air masses for a flood
forecasting system', International Journal of Remote Sensing, Vol 18(4), pp 887 - 898.
Peak, J.E and Aha, D.W. (1996) ‘AIRIES 96 Workshop on artificial intelligence research in
environmental science’, AI Applications, Vol 11(1), pp 103 - 111.
Phien, H.N. and Prahan, P.S.S. (1983) ‘The tank model in rainfall-runoff modelling’, Water SA, Vol
9(3), pp 93 - 102.
Poff, N.L. Tokar, A.S. and Johnson, P. (1996) 'Stream hydrological and ecological responses to
climate change assessed with an artificial neural network', Limnology and Oceanography, Vol
41(5), pp 857 - 863.
Rajurkar, M.P., Kothyari, U.C. and Chaube, U.C. (2002) ‘Artificial neural networks for daily
rainfall-runoff modelling’, Hydrological Sciences Journal, Vol 47(6), pp 865 - 877.
Rajurkar, M.P. Kothyari, U.C. and Chaube, U.C. (2004) ‘Modeling of the daily rainfall-runoff
relationship with artificial neural network’, Journal: Journal of Hydrology, Vol 285(1-4), pp 96 –
113.
Raman, H. and Sunilkumar, N. (1995) 'Multivariate Modelling of Water Resources Time Series
Using Artificial Neural Networks', Hydrological Sciences, Vol 40(2), pp 145 - 163.
Ramesh, T.S.V. and Mujumdar, P.P. (1996) ‘Rainfall forecasting using neural networks’, Stochastic
Hydraulics, Tickle (ed), Balkema, Rotterdam, ISBN 9054108177, pp 325 - 332.
Ranjithan, S. Eheart, J.W. and J.H. Garrett. (1993) 'Neural network-based screening for groundwater
reclamation under uncertainty', Water Resources Research, Vol 29(3), pp 563 - 574.
Ranjithan, S. Eheart, J.W. and J.H. Garrett (1995) 'Application of neural network in groundwater
remediation under conditions of uncertainty', In: Kundzewicz, Z.W. (ed), New uncertainty
concepts in hydrology and water resources, Cambridge University Press, ISBN: 0-521-46118-9,
pp 133 - 140.
Recknagal, F. French, M. Harkonen, P. and Yabunaka, K. (1997) ‘Artificial neural network approach
for modelling and prediction of algal blooms’, Ecological Modelling, Vol 96(1-3), pp 11-28.
Refsgaard, J.C. Knudsen, J. (1996) ‘Operational validation and intercomparison of different types of
hydrological models’, Water Resources Research, Vol 32(7), pp 2189 - 220.
Retnam, M.T.P. and Williams, B.J. (1988) ‘Input errors in rainfall-runoff modelling’, Mathematics
and Computers in Simulation, Vol 30 (1-2), pp 119 - 131.
Rizzo, D.M. and Dougherty, D.E. (1994) ‘Characterization of aquifer properties using artificial
neural networks: neural kriging’, Water Resources Research, Vol 30(2), pp 483 - 497.
Rogers, L.L. and Dowla, F.U. (1994) ‘Optimization of groundwater remediation using artificial
neural networks with parallel solute transport modelling’, Water Resources Research, Vol 30, pp
457 - 481.
Ruifang, Z. (1990) ‘The flood forecasting system for the Three Gorges of the Yangtze River’, The
Hydrological Basis for Water Resources Management, Proceedings of the Beijing Symposium,
October, IAHS Publication No 197, pp 485 - 495.
Sajikumar, N. and Thandaveswara, B.S. (1999) 'A non-linear rainfall-runoff model using artificial
neural networks', Journal of Hydrology, Vol 216(1-2), pp 32 - 55.
Savic, D.A. Walters, G.A. and Davidson, J.W. (1999) 'A genetic programming approach to rainfallrunoff modelling', Water Resources Management, Vol 13(3), pp 219 - 231.
Sawyer, C.S. Achenie, L.E.K. and Lieuallen, K.K. (1995) 'Estimation of aquifer hydraulic
conductivities: a neural network approach' [case study on heterogeneous aquifer in Connecticut,
USA], In: Wagner, B.J. Illangasekare, T.H. and Jensen, K.H. (eds): Models for assessing and
monitoring groundwater quality, IAHS-AISH, Wallingford, Vol 227, pp 177 - 184.
Schaap, M.G. and Bouten, W. (1996) 'Modelling water retention curves of sandy soils using neural
networks', Water Resources Research, Vol 32(10), pp 3033 - 3040.
See, L. Corne, S. Dougherty, M. and Openshaw, S. (1997) 'Some initial experiments with neural
network models of flood forecasting on the River Ouse', Proceedings of the 2nd International
Conference on Geocomputation, 26 - 29 August, University of Otago, Dunedin, New Zealand,
pp 59 - 67.
See, L. Abrahart, R.J. and Openshaw, S. (1998) 'An integrated neuro-fuzzy statistical approach to
hydrological modelling', Proceedings of the 3rd International Conference on Geocomputation,
University of Bristol, 17-19 September,
See, L. and Abrahart, R.J. (1999) ‘Multi-model data fusion for hydrological forecasting’,
Proceedings of the 4th International Conference on Geocomputation, Fredericksburg, Virginia,
USA, 25-28 July.
See, L. and Openshaw, S. (1998) 'Using soft computing techniques to enhance flood forecasting on
the River Ouse', Hydroinformatics 98, Babovic, V. and Larsen, L.C. (eds), Proceedings of the
Third International Conference on Hydroinformatics, 24-26 August, Copenhagen, Denmark, pp
819 - 824.
See, L. and Openshaw, S. (1999) 'Applying soft computing approaches to river level forecasting',
Hydrological Sciences Journal, Vol 44(5), pp 763 - 778.
See, L. and Openshaw, S. (2000) 'A hybrid multi-model approach to river level forecasting',
Hydrological Sciences Journal, Vol 45(4), pp 523 - 536.
See, L. and Abrahart, R.J. (2001) 'Multi-model data fusion for hydrological forecasting', Computers
and Geosciences, Vol 27(8), pp 987 - 994.
Shamseldin, A.Y. and O’Connor, K.M. (1996) ‘A nearest neighbours linear perturbation model for
river flow forecasting’, Journal of Hydrology, Vol 179, pp 353 - 375.
Shamseldin, A.Y. (1997) 'Application of a neural network technique to rainfall-runoff modelling',
Journal of Hydrology, Vol 199, pp 272 - 294.
Shamseldin, A.Y. O’Connor, K.M. and Liang, G.C. (1997) ‘Methods for combining the outputs of
different rainfall-runoff models’, Journal of Hydrology, Vol 197(1-4), pp 203 - 229.
Shamseldin, A.Y. and O’Connor, K.M. (1999) ‘A real-time combination method for the outputs of
different rainfall-runoff models’, Hydrological Sciences Journal, Vol 44(6), pp 895 - 912.
Shin, H.S. and Salas, J.D. (2000) 'Regional drought analysis based on neural networks', Journal of
Hydrologic Engineering, Vol 5(2), pp 145 - 155.
Sivapragasam, C. Liong, S.Y. Pasha, M.F.K. (2001) 'Rainfall and runoff forecasting with SSA-SVM
approach', Journal of Hydroinformatics, Vol 3(3), pp 141 - 152.
Smakhtin, V.Y. Sami, K. and Hughes, D.A. (1998) ‘Evaluating the performance of a deterministic
daily rainfall-runoff model in a low-flow context’, Hydrological Processes, Vol 12(5), pp 797 811.
Smith, J. and Eli, N. (1995) 'Neural-Network Models of Rainfall-Runoff Process', Journal of Water
Resources Planning and Management, Vol 121(6), 499 - 508.
Solomatine, D.P. and Avila Torres, L.A. (1996) ‘Neural network approximation of a hydrodynamic
model in optimizing reservoir operation’, in Muller, A. (ed), Hydroinformatics 96, Proceedings
2nd International Conference on Hydroinformatics, Zurich, Vol 1, pp 201 - 206.
Sorooshian, S. (1991) ‘Parameter estimation, model identification, and model validation: conceptualtype models’, In Bowles, D.S. and O'Connell, P.E. (Eds.) Proceedings of the NATO Advanced
Study Institute on Recent Advances in the Modelling of Hydrologic Systems. Sintra, Portugal,
July 10-23, 1988. Kluwer Academic Publishers.
Stüber, M. and Gemmer, P. (1997) 'An approach for data analysis and forecasting with neuro fuzzy
systems - demonstrated on flood events at River Mosel', Lecture Notes in Computer Science
1226, Computational Intelligence, pp 468 - 477.
Sudheer, K.P. Gosain, A.K. and Ramasastri, K.S. (2002) ‘A data-driven algorithm for constructing
artificial neural network rainfall-runoff models’, Hydrological Processes, Vol 16, pp 1325 1330.
Sudheer, K.P. Nayak, P.C. and Ramasastri, K.S. (2003) ‘Improving peak flow estimates in artificial
neural network river flow models’, Hydrological Processes, Vol 17(3), pp 677 – 686.
Sudheer, K.P. and Jain, A. (2004) ‘Explaining the internal behaviour of artificial neural network
river flow models’, Hydrological Processes, Vol 18, pp 833 – 844.
Sugawara, M. and Ozaki, E. (1991) ‘Runoff analysis of the Chang Jiang (the Yangtze River)’,
Hydrological Sciences Journal, Vol 36(2), pp 135 - 152.
Sun, X. Mein, R.G. Keenan, T.D. and Elliott, J.F. (2000) ‘Flood estimation using radar and
raingauge data’, Journal of Hydrology, Vol 239(1-4), pp 4 - 18.
Supharatid, S. (2003) ‘Application of a neural network model in establishing a stage-discharge
relationship for a tidal river’, Hydrological Processes, Vol 17, pp 3085 – 3099.
Sureerattanan, S. and Phien, H.N. (1997) 'Back-propagation networks for daily streamflow
forecasting', Water Resources Journal, December, pp 1 - 7.
Tabrizi, M.H.N. (1998) 'Neural Network modeling of an influent inflow system', Proceedings of the
1998 Artificial Networks in engineering Conference, St. Louis, MO, 1 Nov - 4 Nov, Source:
Intelligent Engineering Systems Through Artificial Neural Networks, Vol 8, ASME, pp 153 158.
Tawfik, M. Ibrahim, A. and Fahmy, H. (1997) 'Hysteresis sensitivity neural network for modeling
rating curves', Journal of Computing in Civil Engineering, Vol 11(3), pp 206 - 211.
Tayfur, G. (2002) ‘Artificial neural networks for sheet sediment transport’, Hydrological Sciences
Journal, Vol 47(6), pp 879 - 892.
Thandaveswara, B. S. and Sajikumar, N. (2000) ‘Classification of River Basins Using Artificial
Neural Network’, Journal of Hydrologic Engineering, Vol 5(3), pp 290 - 298.
Thirumalaiah, K. (1997) 'Application of artificial neural networks and object oriented programming
to hydrological forecasting’, PhD thesis, Indian Institute of Technology, Bombay, India.
Thirumalaiah, K. and Deo, M.C. (1998a) 'Real-time flood forecasting using neural networks',
Computer-Aided Civil and Infrastructure Engineering, Vol 13(2), pp 101 - 111.
Thirumalaiah, K. and Deo, M.C. (1998b) 'River stage forecasting using artificial neural networks',
Journal of Hydrologic Engineering, Vol 3(1), pp 26 - 32.
Thirumalaiah, K. and Deo, M.C. (1998c) ‘Application of object oriented programming to on-line
hydrological forecasting’, Journal of Hydraulics, Indian Society of Hydraulics, Pune, India, Vol
4(1), pp 49 - 60.
Thiumalaiah, K. and Deo, M.C. (2000) 'Hydrological forecasting using neural networks', Journal of
Hydrologic Engineering, Vol 5(2), pp 180 - 189.
Thomas, W.O. (1982) ‘An evaluation of flood frequency estimates based on rainfall/runoff
modeling’, Water Resources Bulletin, Vol 18(2), pp 221 - 230.
Tingsanchali, T. and Gautam, M.R. (2000) ‘Application of tank, NAM, ARAM and neural network
models to flood forecasting’, Hydrological Processes, Vol 14, pp 2473 - 2487.
Todini, E. (1988) ‘Rainfall-runoff modeling - past, present and future’, Journal of Hydrology, Vol
100(1-4), pp 341 - 352.
Tokar, A.S. and Johnson, P.A. (1999) ‘Rainfall-Runoff Modeling Using Artificial Neural Networks’,
Journal of Hydrologic Engineering, Vol 4(3), pp 232 - 239.
Tokar, A.S. and Markus, M. (2000) 'Precipitation-runoff modeling using artificial neural networks
and conceptual models', Journal of Hydrologic Engineering, Vol 5(2), pp 156 - 161.
Toth, E. Montanari, A. and Brath, A. (1999) ‘Real-time flood forecasting via combined use of
conceptual and stochastic models’, Physics and Chemistry of the Earth (B), Vol 24, pp 793 - 798.
Toth, E. Brath, A. and Montanari, A. (2000) ‘Comparison of short-term rainfall prediction models
for real-time flood forecasting’, Journal of Hydrology, Vol 239(1-4), pp 132 - 147.
Trigo, R.M. and Palutikof, J.P. (1999) ‘Simulation of daily temperatures for climate change scenarios
over Portugal: a neural network model approach’, Climate Research, Vol 13, pp 45 - 59.
Uvo, C.B. Tolle, U. and Berndtsson, R. (2000) ‘Forecasting discharge in Amazonia using artificial
neural networks’, International Journal of Climatology, Vol 20(12), pp 1495 - 1507.
Vivekanandan, N. (2000) ‘Streamflow forecasting using linear perturbation model & ANN
approach’, Unpublished Master of Hydrology Thesis, Department of Hydrology, University of
Roorkee, Roorkee-247-667, India.
Wang, G.T. and Chen, S. (1996) ‘A linear spatially distributed model for a surface rainfall-runoff
system’, Journal of Hydrology, Vol 185(1-4), pp 183 - 198.
Wang, Q.J. (1991) ‘The genetic algorithm and its application to calibrating conceptual rainfall-runoff
models’, Water Resources Research, Vol 27(9), pp 2467 - 2471.
Watts, G. (1997) 'Hydrological modelling'. In Wilby, R.L.(ed.) 'Contemporary hydrology: towards
holistic environmental science', Chapter 5. Wiley, Chichester.
Wen, C.G. and Lee, C.S. (1998) ‘A neural network approach to multiobjective optimization for
water quality management in a river basin’, Water Resources Research, Vol 34(3), pp 427 -436.
Whitehead, P.G. Howard, A. and Arulmani, C. (1997) ‘Modelling algal growth and transport in
rivers: a comparison of time series analysis, dynamic mass balance and neural network
techniques’, Hydrobiologia, Vol 349, pp 39 - 46.
Whiteley, R. and Hromadka, T.V. (1999) 'Approximate confidence intervals for design floods for a
single site using a neural network', Water Resources Research, Vol 35(1), pp 203 - 209.
Wilke, K. and Barth, F. (1991) ‘Operational river-flood forecasting by Wiener and Kalman filtering’,
Hydrology for the Water Management of Large River Basins, Proceedings of the Vienna
Symposium, August, IAHS Publication No. 201, pp 391 - 400.
Williams, B.J. and Field, W.G. (1985) ‘Rainfall runoff models in flood forecasting applications’,
Mathematics and Computers in Simulation, Vol 27, pp 159 - 165.
Wong, T.S.W. discussing O’Loughlin, G. Huber, W. and Chocat, B. (1998) ‘Rainfall-runoff
processes and modelling - Discussion’, Journal of Hydraulic Research, Vol 36(2), pp 281 - 283.
Wood, E.F. and O'Connell, P.E. (1985) ‘Real time forecasting’, In Anderson, M.G. and Burt, T.P.
(Eds.) Hydrological forecasting, Chapter 15. Wiley, Chichester.
World Meteorological Organisation (1992) ‘Simulated Real-Time Intercomparison of Hydrological
Models’, Operational Hydrology Report No. 38, WMO 779, Geneva.
Wright N. G. and Dastorani, M. T. (2001) 'Effects of river basin classification on Artificial Neural
Networks based ungauged catchment flood prediction', Proceedings of the International
Symposium on Environmental Hydraulics, December 5-8, 2001, Phoenix, USA.
Xiao, R. (1997) ‘Development of a Neural Network Based Algorithm for Rainfall Estimation from
Radar Observations’, IEEE Trans on Geoscience and Remote Sensing, Vol 35(1), 160 - 171.
Xiong, L. and O'Connor, K.M. (2002) ‘Comparison of four updating models for real-time river flow
forecasting’, Hydrological Sciences Journal, Vol 47(4), pp 621 - 639.
Xu, C.Y. and Vandewiele, G.L. (1994) ‘Sensitivity of monthly rainfall-runoff models to input errors
and data length’, Hydrological Sciences Journal, Vol 39(2), pp 157 - 176.
Xu, C.Y. (1999) ‘Climate change and hydrologic models: a review of existing gaps and recent
research developments’, Water Resources Management, Vol 13, pp 369 - 382.
Yang, R. (1997) 'Application of neural networks and genetic algorithms to modelling flood
discharges and urban water quality', unpublished PhD thesis, University of Manchester, UK.
Yapo, P.O. Gupta, H.V. and Sorooshian, S. (1996) ‘Automatic calibration of conceptual rainfallrunoff models: sensitivity to calibration data’, Journal of Hydrology, Vol 181, pp 23 - 48.
Yu, B. Ciesiolka, C.A.A. Rose, C.W. and Coughlan, K.J. (1997) ‘A note on sampling errors in the
rainfall and runoff data collected using tipping bucket technology’, Transactions of the ASAE,
Vol 40(5), pp 1305 - 109.
Zealand, C. M. Burn, D.H. and Simonovic, S.P. (1999) 'Short term streamflow forecasting using
artificial neural networks', Journal of Hydrology, Vol 214(1-4), pp 32 - 48.
Zhang, S.P. Watanabe, H. and Yamada, R. (1993) ‘Prediction of daily water demands by neural
networks’, in Stochastic and Statistical Methods in Hydrology and Environmental Engineering,
Hipel, K.W. et al (eds), Vol 3, pp 217 - 227.
Zhang, B. and Govindaraju, R.S. (2000) ‘Prediction of watershed runoff using Bayesian concepts
and modular neural networks’, Water Resources Research, Vol 36(3), pp 753 - 762.
Zhu, M. and Fujita, M. (1993) ‘A comparison of fuzzy inference method and neural network method
for runoff prediction’, Proceedings of Hydraulic Engineering, JSCE, Vol 37, pp 75-80.
Zhu, M. Fujita, M. and Hashimoto, N. (1994) ‘Application of neural networks to runoff prediction’,
in Stochastic and Statistical Methods in Hydrology and Environmental Engineering, Hipel, K.W.
et al (eds), Vol 3, pp 205 - 216.