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Filling up gaps in wave data with genetic programming

DSpace at IIT Bombay

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Title Filling up gaps in wave data with genetic programming
 
Creator USTOORIKAR, KETAKI
DEO, MC
 
Subject correlation methods
neural networks
genetic programming
information systems
 
Description A given time series of significant wave heights invariably contains smaller or larger gaps or missing values due to a variety of reasons ranging from instrument failures to loss of recorders following human interference. In-filling of missing information is widely reported and well documented for variables like rainfall and river flow, but not for the wave height observations made by rider buoys. This paper attempts to tackle this problem through one of the latest soft computing tools, namely, genetic programming (GP). The missing information in hourly significant wave height observations at one of the data buoy stations maintained by the US National Data Buoy Center is filled up by developing GP models through spatial correlations. The gap lengths of different orders are artificially created and filled up by appropriate GP programs. The results are also compared with those derived using artificial neural networks (ANN). In general, it is found that the in-filling done by GP rivals that by ANN and many times becomes more satisfactory, especially when the gap lengths are smaller. Although the accuracy involved reduces as the amount of gap increases, the missing values for a long duration of a month or so can be filled up with a maximum average error up to 0.21 m in the high seas.
 
Publisher Elsevier
 
Date 2009-03-23T09:45:57Z
2011-11-25T20:22:28Z
2011-12-26T13:08:10Z
2011-12-27T05:56:10Z
2009-03-23T09:45:57Z
2011-11-25T20:22:28Z
2011-12-26T13:08:10Z
2011-12-27T05:56:10Z
2008
 
Type Article
 
Identifier Marine Structures 21(2-3), 177-195
0951-8339
http://dx.doi.org/10.1016/j.marstruc.2007.12.001
http://hdl.handle.net/10054/1092
http://dspace.library.iitb.ac.in/xmlui/handle/10054/1092
 
Language en