Record Details

Prediction of breaking waves with neural networks

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title Prediction of breaking waves with neural networks
 
Creator DEO, MC
JAGDALE, SS
 
Subject graphic methods
neural networks
water levels
water wave effects
 
Description The height of a wave at the time of its breaking, as well as the depth of water in which it breaks, are the two basic parameters that are required as input in design exercises involving wave breaking. Currently the designers obtain these values with the help of graphical procedures and empirical equations. An alternative to this in the form of a neural network is presented in this paper. The networks were trained by combining the existing deterministic relations with a random component. The trained network was validated with the help of fresh laboratory observations. The validation results confirmed usefulness of the neural network approach for this application. The predicted breaking height and water depth were more accurate than those obtained traditionally through empirical schemes. Introduction of a random component in network training was found to yield better forecasts in some validation cases.
 
Publisher Elsevier
 
Date 2009-03-23T05:11:00Z
2011-11-25T20:01:25Z
2011-12-26T13:07:38Z
2011-12-27T05:55:40Z
2009-03-23T05:11:00Z
2011-11-25T20:01:25Z
2011-12-26T13:07:38Z
2011-12-27T05:55:40Z
2003
 
Type Article
 
Identifier Ocean Engineering 30(9), 1163-1178
0029-8018
http://dx.doi.org/10.1016/S0029-8018(02)00086-0
http://hdl.handle.net/10054/1050
http://dspace.library.iitb.ac.in/xmlui/handle/10054/1050
 
Language en