Parameter Optimization Using GA in SVM to Predict Damage Level of Non-Reshaped Berm Breakwater
In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA algorithm. The models are trained and tested on the data se...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2014-06-01
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| Series: | International Journal of Ocean and Climate Systems |
| Online Access: | https://doi.org/10.1260/1759-3131.5.2.79 |
| Summary: | In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA algorithm. The models are trained and tested on the data set obtained from the experiments which were carried out at Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. The results of SVM and GA-SVM models are compared in terms of statistical measures like correlation coefficient, root mean square error and scatter index. The results on SVM and GA-SVM models reveals that the performance of GA-SVM is better compared to SVM models in predicting the damage level of non-reshaped berm breakwater. |
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| ISSN: | 1759-3131 1759-314X |