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

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  • Mansi Daga
  • M. Deo

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. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • Mansi Daga & M. Deo, 2009. "Alternative data-driven methods to estimate wind from waves by inverse modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(2), pages 293-310, May.
    • Handle: RePEc:spr:nathaz:v:49:y:2009:i:2:p:293-310
    DOI: 10.1007/s11069-008-9299-2
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    Cited by:

    1. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.

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