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Genetic Programming to Estimate Coastal Waves from Deep Water Measurements
Satellites gather vast quantities of ocean wave data worldwide and such measurements are available to ocean scientists and engineers at low costs. However corresponding information is more useful in deeper sea with open or exposed locations rather than nearshore locations involving complex bathymetric effects. The technique based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) to map remote sensed deep-water waves with coastal waves was attempted by the authors in the past (Kalra et al (2005, a, b)). This paper presents an application of a relatively new soft computing tool called Genetic Programming for this purpose. Significant wave heights at a number of locations over a track parallel to the coastline are used to estimate the significant wave heights at a nearshore site. The success of the method adopted was confirmed from the satisfactory error measures it produced during the testing carried out following the training. The results are also compared with those derived using artificial neural networks (ANN). In general it was found that the spatial mapping of wave heights done by genetic programming rivals that by ANN.
Wave data, wave mapping, geometric programming, neural networks
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