By Robert Abrahart, P.E. Kneale, Linda M. See
A new method of the fast-developing international of neural hydrological modelling, this booklet is key interpreting for teachers and researchers within the fields of water sciences, civil engineering, hydrology and actual geography. each one bankruptcy has been written by means of a number of eminent specialists operating in numerous fields of hydrological modelling. The booklet covers an advent to the strategies and know-how concerned, a variety of case-studies with useful purposes and techniques, and finishes with suggestions for destiny study instructions. Wide in scope, this ebook deals either major new theoretical demanding situations and an exam of real-world problem-solving in all parts of hydrological modelling interest.
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A brand new method of the fast-developing international of neural hydrological modelling, this booklet is key examining for teachers and researchers within the fields of water sciences, civil engineering, hydrology and actual geography. each one bankruptcy has been written through a number of eminent specialists operating in numerous fields of hydrological modelling.
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In A. ), Proceedings 2nd International Conference on Hydroinformatics, Zurich, Switzerland, 9–13 September 1996. Vol. 1. 201–206. A. Balkema. , Ibrahim, A. & Fahmy, H. 1997. Hysteresis sensitivity neural network for modeling rating curves. Journal of Computing in Civil Engineering 11(3): 206–211. G. 1993. The Promise of Neural Networks. London: Springer-Verlag. A. A. 1999. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering 4(3): 232–239. R. 2003. Backpropagator’s Review.
G. year type such as wet or dry (Tokar & Johnson, 1999); percentage impervious area (Minns, 1996); or storm occurrence (Dawson & Wilby, 1998). In order to improve performance, the neurohydrologist must first establish the optimal lag-interval between input and response. g. , 1997) or autocorrelation functions. Auto Regressive Moving Average (ARMA) models are often used to determine appropriate variables, lead times and the optimal window(s) for averaging (Maier & Dandy, 2000). Alternatively, correlation testing may be used to identify the strongest causal relationships from a set of possible predictor variables (as in Dawson & Wilby, 1998).
IEEE Transactions on Neural Networks 3(4): 624–627. Y. T. 1993. Runoff Volume Estimates with Neural Networks. V. I. ), Neural Networks and Combinatorial Optimization in Civil and Structural Engineering, Proceedings Third International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, Edinburgh, UK, 17–19 August 1993, 67–70. T. S. 1994. Regional Estimation of Floods for Ungaged Catchments with Neural Networks. In H-F. J. Shankar, E-S. Chan & W-J. ), Developments in Hydraulic Engineering and their impact on the Environment, Proceedings Ninth Congress of the Asian and Pacific Division of the International Association for Hydraulic Research, Singapore, 24–26 August 1994, 372–378.