Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.
IR@NIO: CSIR-National Institute Of Oceanography, Goa
View Archive Info| Field | Value | |
| Creator |
Harish, N.
Lokesha. Mandal, S. Rao, S. Patil, S.G. |
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| Date |
2014-09-04T07:07:55Z
2014-09-04T07:07:55Z 2014 |
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| Identifier |
International Journal of Ocean and Climate Systems, vol.5(2); 2014; 79-88.
http://drs.nio.org/drs/handle/2264/4589 |
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| Description |
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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| Language |
en
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| Publisher |
Multi-Science Publishing Company
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| Rights |
Copyright [2014]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
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| Subject |
Genetic Algorithm
water level |
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| Title |
Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.
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| Type |
Journal Article
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