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Real Time wave forecasting using artificial neural network with varying input parameter

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Title Real Time wave forecasting using artificial neural network with varying input parameter
 
Creator Vimala, J.
Latha, G.
Venkatesan, R.
 
Subject ANN
Wave Forecasting
Correlation coefficient
Neural network
Computational elements
 
Description 82-87
Prediction of significant wave heights
(Hs) is of immense importance in ocean and coastal engineering applications. The
aim of this study is to predict significant wave height values at buoy
locations with the lead time of 3,6,12 and 24 hours using past observations of
wind and wave parameters applying Artificial Neural Network. Although there
exists a number of wave height estimation models, they do not consider all
causative factors without any approximation and consequently their results are
more or less a general approximation of the overall dynamic behaviour. Since
soft computing techniques are totally data driven, based on the duration of the
data availability they can be used for prediction. In the National data buoy
program of National institute
of Ocean Technology, not
all the buoys have wind sensors and wave sensors and so it is attempted to
apply neural network algorithms for prediction of wave heights using wind speed
only as the input and then using only wave height as the input. The measurement
made by the data buoy at DS3 location in Bay of Bengal
(12o11’21"N and 90o43’33"E) are considered, for
the period 2003-2004. Out of this, the data of period Jan 2003-Dec 2003 was
used for training and the data for the period July 2004- Nov 2004 is used for
testing. Real time wave forecasting for 3,6,12 and 24 hours were carried out
for a month at the location chosen and the results show that the ANN technique
proves encouraging for wave forecasting. Performance of ANN for varying inputs
have been analysed and the results are discussed.
 
Date 2014-02-05T09:32:57Z
2014-02-05T09:32:57Z
2014-01
 
Type Article
 
Identifier 0975-1033 (Online); 0379-5136 (Print)
http://hdl.handle.net/123456789/26430
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source IJMS Vol.43(1) [January 2014]