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|dc.identifier.issn||0975-1033 (Online); 0379-5136 (Print)||-|
|dc.description.abstract||Present 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.source||IJMS Vol.38(2) [June 2009]||en_US|
|dc.title||Development of a neural network model for dissolved oxygen in seawater||en_US|
|Appears in Collections:||IJMS Vol.38(2) [June 2009]|
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