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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Deo, M C | - |
| dc.date.accessioned | 2011-01-04T09:04:06Z | - |
| dc.date.available | 2011-01-04T09:04:06Z | - |
| dc.date.issued | 2010-12 | - |
| dc.identifier.issn | 0975-1033 (Online); 0379-5136 (Print) | - |
| dc.identifier.uri | http://hdl.handle.net/123456789/10807 | - |
| dc.description | 589-596 | en_US |
| dc.description.abstract | Artificial 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.iso | en_US | en_US |
| dc.publisher | NISCAIR-CSIR, India | en_US |
| dc.rights | CC Attribution-Noncommercial-No Derivative Works 2.5 India | en_US |
| dc.source | IJMS Vol.39(4) [December 2010] | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Ocean parameters | en_US |
| dc.subject | Ocean predictions | en_US |
| dc.title | Artificial neural networks in coastal and ocean engineering | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | IJMS Vol.39(4) [December 2010] | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| IJMS 39(4) 589-596.pdf | 161.9 kB | Adobe PDF | View/Open |
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