Abstract
In recent years, Artificial Neural Networks have emerged as a powerful data driven approach of modelling and predicting complex physical and biological systems. The approach has several advantages over other traditional data driven approaches. Particularly among them are the facts that they can be used to model non-linear processes and that they do not require ’a priori’ understanding of the detailed mechanics of the processes involved. Because of the parallel nature of the data processing, the approach is also quite robust and insensitive to noise present in the data. Several riverflow applications of ANN’s are presented in this paper.
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Jayawardena, A.W. (2009). Riverflow Prediction with Artificial Neural Networks. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_44
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DOI: https://doi.org/10.1007/978-3-642-03969-0_44
Publisher Name: Springer, Berlin, Heidelberg
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