An attempt is made to derive wind speed from wave measurements by carrying out an inverse modeling. This requirement arises out of difficulties occasionally encountered in collecting wave and wind data simultaneously. The wind speed at every 3-h interval is worked out from corresponding simultaneous measurements of significant wave height and average wave periods with the help of alternative data-driven methods such as program-based genetic programming, model trees, and locally weighted projection regression. Five different wave buoy locations in Arabian Sea, representing nearshore and offshore as well as shallow and deep water conditions, are considered. The duration of observations ranged from 15 months to 29 months for different sites. The testing performance of calibrated models has been evaluated with the help of eight alternative error statistics, and the best model for all locations is determined by averaging out the error measures into a single evaluation index. All the three methods satisfactorily estimated the wind speed from known wave parameters through inverse modeling. The genetic programming is found to be the most suitable tool in majority of the cases.
We thank the discussers for their comments. Our objective in this article was to evaluate the performance of different data-driven methods, including genetic programming (GP) in carrying out inverse modeling tasks. We tried to explain the basic idea of GP in general terms. While the code used by us was based on a variant of GP, as pointed out by the discussers, the extension of genetic algorithm to genetic programming can always be explained in general terms for the benefit of unfamiliar readers, as done by us.We, however, agree with the discusser in that there exists a scope to take the lead of applying GP further from this study, and this may include studying if team solutions can yield better performance and so on.M. Daga Á M. C. Deo
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