Hybrid genetic algorithm tuned support vector machine regression for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater

Support Vector Machine (SVM) works on structural risk minimization principle that has greater generalization ability and is superior to the empirical risk minimization principle as adopted in conventional neural network models. However, it is noticed that one particular model in isolation cannot capture all data patterns easily. In the present paper, a hybrid genetic algorithm tuned support vector machine regression (HGASVMR) model was developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, parameters of both linear and nonlinear SVM models are determined by Genetic Algorithm. HGASVMR model was trained on the dataset obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Surathkal, India. The results are compared with artificial neural network (ANN) model in terms of Correlation Coefficient, Root Mean Square Error and Scatter Index. Performance of HGASVMR is found to be reliably superior.