Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/10807
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDeo, M C-
dc.date.accessioned2011-01-04T09:04:06Z-
dc.date.available2011-01-04T09:04:06Z-
dc.date.issued2010-12-
dc.identifier.issn0975-1033 (Online); 0379-5136 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/10807-
dc.description589-596en_US
dc.description.abstractArtificial neural network (ANN)s have been applied to solve a variety of problems related to the coastal and ocean areas over a period of last one and a half decades. Most of these studies have involved estimation or forecasting of environmental parameters, structural loads and responses. The aim in such studies were to carry out a cause-effect modeling, temporal or spatial mapping, and property integration and resolution. ANN models have been generally found to outperform the traditional empirical, statistical or numerical models to a smaller or larger extent. There are however certain grey areas where the modeling and its usefulness needs to be improved. This paper takes a stock of all such past works and summarizes author’s experience in working with ANNs for the benefit of future researchers.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJMS Vol.39(4) [December 2010]en_US
dc.subjectArtificial neural networksen_US
dc.subjectOcean parametersen_US
dc.subjectOcean predictionsen_US
dc.titleArtificial neural networks in coastal and ocean engineeringen_US
dc.typeArticleen_US
Appears in Collections:IJMS Vol.39(4) [December 2010]

Files in This Item:
File Description SizeFormat 
IJMS 39(4) 589-596.pdf161.9 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.