Neuro-fuzzy approach for the spatial estimation of ocean wave characteristics

Real time applications of oceanographic data and statistical calculations for climatological studies suffer from missing data. To overcome this problem, spatial correlation of significant wave height from multiple stations is considered. Here significant wave height is taken as regionalized variable to establish a relationship through the use of fuzzy logic. There are other techniques in the literature to explore spatial dependency such as regression, Kriging and artificial neural networks. Spatial variability of wave characteristics is also important for ocean wave energy applications. It is shown that significant wave heights at the location of one buoy can be predicted using the neuro-fuzzy approach and the data from adjacent buoys. The proposed approach is compared with the multiple linear regression model which includes some restrictive assumptions. The implementation of the methodology is performed for the buoys located in the Gulf of Mexico.

[1]  Z. Şen,et al.  Regional wind energy evaluation in some parts of Turkey , 1998 .

[2]  Stan Openshaw,et al.  A hybrid multi-model approach to river level forecasting , 2000 .

[3]  Zekâi Sen,et al.  Cumulative semivariogram models of regionalized variables , 1989 .

[4]  O. Makarynskyy,et al.  Improving wave predictions with artificial neural networks , 2004 .

[5]  Chang Lin,et al.  Neural network for wave forecasting among multi-stations , 2002 .

[6]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Mehmet Özger,et al.  Prediction of wave parameters by using fuzzy logic approach , 2007 .

[8]  Luigi Cavaleri,et al.  An Atlas of the Wave-Energy Resource in Europe , 1996 .

[9]  Philippe Bonneton,et al.  Spatial Variability of Wave Conditions on the French Atlantic Coast using In-Situ Data , 2002, Journal of Coastal Research.

[10]  V. Panchang,et al.  Correlation of wave data from buoy networks , 2007 .

[11]  C. Guedes Soares,et al.  Bivariate autoregressive models for the time series of significant wave height and mean period , 2000 .

[12]  Marley M. B. R. Vellasco,et al.  A neuro-fuzzy evaluation of steel beams patch load behaviour , 2008, Adv. Eng. Softw..

[13]  Z. Şen,et al.  Regional assessment of wind power in western Turkey by the cumulative semivariogram method , 1997 .

[14]  Richard Burrows,et al.  Wave predictions based on scatter diagram data. A computer program package , 1995 .

[15]  H. Md. Azamathulla,et al.  Alternative neural networks to estimate the scour below spillways , 2008, Adv. Eng. Softw..

[16]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[17]  Chimay J. Anumba,et al.  A fuzzy system for evaluating the risk of change in construction projects , 2006, Adv. Eng. Softw..

[18]  M. T. Pontes,et al.  Assessing the European Wave Energy Resource , 1998 .

[19]  V. Panchang,et al.  One-Day Wave Forecasts Based on Artificial Neural Networks , 2006 .

[20]  M. C. Deo,et al.  Estimation of wave spectral shapes using ANN , 2005, Adv. Eng. Softw..

[21]  James H. Wilson,et al.  Parameter variation and part-load efficiencies of wave energy conversion , 2006 .

[22]  M J Tucker,et al.  WAVES IN OCEAN ENGINEERING , 2001 .