Artificial neural networks (ANN) is probably the most
successful technology develeloped in the framework of Artificial
Intelligence (AI). ANN are widely applied in pattern recognition,
control and modelling.
Research in ANN is aimed at applying various types
of ANN to solving practical problems in water resources modelling
and management.
In the recent publications several applications of
ANN were demonstrated. Neural network tool NNN (for MS-DOS
and for Windows) has been built. A version that can be run across
Internet (WebNNN) is also available.
Solomatine, D P, and Avila Torres,
L A, (1996). Neural network
approximation of a hydrodynamic model in optimizing reservoir
operation. Proc. 2nd Int. Conf. on Hydroinformatics, Zurich, September
9-13.
Price R.K., Samedov J., Solomatine D.P.Network modelling using
artificial neural networks. Proc. Intern Conf. Hydroinformatics-98,
Balkema, Rotterdam, 1998.
Shen Y., D.P. Solomatine, H. van den Boogaard. Improving performance
of chlorophyl concentration time series simulation with artificial
neural networks. Annual Journal of Hydraulic Engineering, JSCE,
vol. 42, 1998, February, pp. 751-756.
Dibike Y.B., Solomatine D.P.River flow forecasting using artificial
neural networks. European Geophysical Society XXIV General Assembly,
The Hague, 19-23 April 1999.
Y.B. Dibike, D.P. Solomatine, M.B. Abbott. On the encapsulation
of numerical-hydraulic models in artificial neural network. Journal
of Hydraulic Research, No. 2, 1999.
Lobbrecht A.H., Solomatine D.P.Control of water levels in polder
areas using neural networks and fuzzy adaptive systems. In: Water
Industry Systems: Modelling and Optimization Applications, D.
Savic, G. Walters (eds.). Research Studies Press Ltd., 1999, pp.
509-518.
B. Bhattacharya, D.P. Solomatine. Application of artificial neural
network in stage-discharge relationship. Proc. 4th Int. Conference
on Hydroinformatics. Iowa, USA, July 2000.
Dibike Y.B. and Solomatine D.P. River Flow Forecasting Using
Artificial Neural Networks, Journal of Physics and Chemistry
of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2001,
Vol. 26, No.1, pp. 1-8.
A.H. Lobbrecht, D.P. Solomatine. Machine learning in real-time
control of water systems. Urban Water 4, 2002, 283-289.
D.P. Solomatine, K.N. Dulal. Model trees as an alternative to
neural networks in rainfall-runoff modeling. Hydrological Sciences
Journal, 48(3), 2003, 399 - 411.
Full texts are here.
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