Alternative data-driven methods to estimate wind from waves by inverse modeling |
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| Authors: | Mansi Daga M C Deo |
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| Institution: | (1) Department of Civil Engineering, Indian Institute of Technology, Bombay, Mumbai, 400076, India |
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| Abstract: | 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. |
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| Keywords: | Locally weighted learning Genetic programming Model trees Inverse modeling Wind estimation |
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