- author = "Arvind K. Singh and M. C. Deo and V. Sanil Kumar",
- title = "Combined neural network--genetic programming for sediment transport",
- journal = "Proceedings of the Institution of Civil Engineers",
- year = "2007",
- volume = "160",
- number = "3",
- pages = "113--119",
- month = sep,
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1741-7597",
-
DOI = "
doi:10.1680/maen.2007.160.3.113",
- size = "7 pages",
- abstract = "The planning, operation, design and maintenance of almost all harbour and coastal engineering facilities call for an estimation of the longshore sediment transport rate. This is currently and popularly done with the help of empirical equations. In this paper an alternative approach based on a combination of two soft computing tools, namely neural networks and genetic programming, is suggested. Such a combination was found to produce better results than the individual use of neural networks or genetic programming. The ability of the neural network to approximate a non-linear function coupled with the efficiency of the genetic programming to make an optimum search over the solution domain seems to result in a better prediction.",
-
notes = "Karwar. NN-GP
'The current study highlighted complexities and uncertainties associated with the problem of prediction of the rate of coastal sediment transport. It has shown that the use of soft computing tools, such as NN or GP can significantly improve the accuracy of prediction compared with the popular regression models. In addition, a further improvement in the quality of prediction can be achieved by a combined NN-GP model. In such a case the NN can first carry out an effective function approximation and later the GP could seek the correct solution through an appropriate search over the solution domain.'
Discussed by \cite{Gandomi:2011:Discussion}
Department of Civil Engineering, IIT Bombay",
