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Significant Deep Wave Height Prediction by using Support Vector Machine Approach (Alexandria as case of study)

Author : Tamer Elgohary and Amir Mubasher and Hassan Salah

Pages : 135-143
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The numerous increase in offshore operational activities demands improved wave forecasting techniques. If the accurate wave data is available, it is possible to carry out the marine activities easily and safely (i.e. offshore drilling, offshore platforms and pipelines installation, naval operations, near shore construction activities, etc.). This paper focuses on the prediction of significant wave heights (Hs ) by support vector machine (SVM), using various kernel functions. This study aims to evaluate the influence of fetch and meteorological data over SVM approach and also perform a comparison between SVM kernel methods. Measured sea waves off Alexandria, west coast of Egypt, and meteorological data are used in this study. Six SVM (linear kernel) models comprising of various input combinations for wind speed, fetch, sea level pressure and air temperature have been performed to evaluate the wave height prediction performance of fetch and meteorological parameter. The results indicated that the SVM model (linear kernel function) gave the satisfactory results with all parameters (wind speed, fetch, sea level pressure and air temperature). Furthermore, the analysis showed that wind speed is the most important parameters for wave prediction. The results showed also that the fetch could also be useful for the wave height estimations, especially when used in combined with wind speed. Furthermore, the SVM kernels named sigmoid and a radial basis function (RBF) comprising all parameters were investigated. The results indicated that the SVM (linear kernel) gave the same results extracted from the SVM (sigmoid kernel). However, SVM (RBF kernel) gave the best prediction performance over other SVM kernels. Their results indicated that the error statistics of SVM models are generally within an acceptable range. Therefore, SVM can be used successfully for prediction of Hs.

Keywords: Deep waves height, wave prediction, support vector machines, linear kernel function, radial basis function, Alexandria waves heights.

Article published in International Journal of Current Engineering and Technology, Vol.7, No.1 (Feb-2017)

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