Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/4664
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dc.contributor.authorPalani, Sundarambal-
dc.contributor.authorLiong, Shie-Yui-
dc.contributor.authorTkalich, Pavel-
dc.contributor.authorPalanichamy, Jegathambal-
dc.date.accessioned2009-06-16T05:30:10Z-
dc.date.available2009-06-16T05:30:10Z-
dc.date.issued2009-06-
dc.identifier.issn0975-1033 (Online); 0379-5136 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/4664-
dc.description151-159en_US
dc.description.abstractPresent paper consists the results from a study conducted to test the adequacy of artificial neural networks in modelling of dissolved oxygen (DO) in seawater. The input variables for ANN DO models are selected by statistical analysis. The ranking of important inputs and their mode of action on the output DO are obtained based on the expert’s opinion. The calibrated neural network models predict the DO concentration with satisfactory accuracy, producing high correlations between measured and predicted values (R2>0.8, MAE<1.25 mg/L for training and overfitting test) at specified location and time in the selected domain where there are training stations. It is shown that one can forecast the next week’s DO level from antecedent measurements with an acceptable confidence.en_US
dc.language.isoen_USen_US
dc.publisherCSIRen_US
dc.sourceIJMS Vol.38(2) [June 2009]en_US
dc.titleDevelopment of a neural network model for dissolved oxygen in seawateren_US
dc.typeArticleen_US
Appears in Collections:IJMS Vol.38(2) [June 2009]

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