Show simple item record

dc.contributor.authorHarish, N.
dc.contributor.authorLokesha.
dc.contributor.authorMandal, S.
dc.contributor.authorRao, S.
dc.contributor.authorPatil, S.G.
dc.date.accessioned2014-09-04T07:07:55Z
dc.date.available2014-09-04T07:07:55Z
dc.date.issued2014
dc.identifier.citationInternational Journal of Ocean and Climate Systems, vol.5(2); 2014; 79-88.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/4589
dc.description.abstractIn 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.
dc.language.isoen
dc.publisherMulti-Science Publishing Company
dc.rightsCopyright [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.
dc.subjectGenetic Algorithm
dc.subjectwater level
dc.titleParameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.
dc.typeJournal Article


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record