Effect of Water Depth on the Performance of Intelligent Computing Models in Predicting Wave Transmission of Floating Pipe Breakwater

Abstract

Understanding the physics of complex system plays an important role in selection of data for training intelligent computing models. Based on the physics of the wave transmission of Horizontally Interlaced Multilayer Moored Floating Pipe Breakwater, a laboratory experiment carried out at Department of Applied Mechanics, National Institute of Technology Surathkal, India, authors felt that relative depth of water ( d/ L) may have effect on the performance of intelligent computing models. In the present paper, d/ L is taken as one of the inputs to study the performance of ANN and Genetic Algorithm based Support Vector Machine Regression (GA-SVMR) model which was ignored by the authors in their previous studies. The performances of present ANN-1 and GA-SVMR-1 models are compared with the previous ANN and GA-SVMR models. The results revealed that there is a slight improvement in the performance of present ANN-1 and GA-SVMR-1 models in terms of Correlation Coefficient.