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Alternative data-driven methods to estimate wind from waves by inverse modeling

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Title Alternative data-driven methods to estimate wind from waves by inverse modeling
 
Creator DAGA, M
DEO, MC
 
Subject neural-networks
locally weighted learning
genetic programming
model trees
inverse modeling
wind estimation
 
Description 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.
 
Publisher SPRINGER
 
Date 2011-08-29T09:06:55Z
2011-12-26T12:58:31Z
2011-12-27T05:48:36Z
2011-08-29T09:06:55Z
2011-12-26T12:58:31Z
2011-12-27T05:48:36Z
2009
 
Type Article
 
Identifier NATURAL HAZARDS, 49(2), 293-310
0921-030X
http://dx.doi.org/10.1007/s11069-008-9299-2
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12036
http://hdl.handle.net/10054/12036
 
Language en