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
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Creator |
USTOORIKAR, KETAKI
DEO, MC |
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Subject |
correlation methods
neural networks genetic programming information systems |
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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.
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Publisher |
Elsevier
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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 |
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Type |
Article
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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 |
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Language |
en
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