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dc.contributor.authorDauji, S-
dc.contributor.authorDeo, M C-
dc.identifier.issn2582-6727 (Online); 2582-6506 (Print)-
dc.description.abstractA method to improve the real time predictions of ocean currents on the basis of a machine learning technique called model tree is proposed. It consists of forming an error time series obtained as the difference between the numerical prediction and the actual measurement of the current at a given time step, carrying out time series prediction as per the technique of model tree and predicting the error for a future time step. Subtraction of such error from the numerically predicted current produces the improved current magnitude for the next time step. The suggested procedure is applied at two deepwater locations in the Indian Ocean. The numerical current model under investigation is code named: HYCOM, while corresponding current observations are those coming from a measurement program called: RAMA. It was found that such method of error subtraction yielded more accurate predictions than those based only on the numerical modelling. This is judged from analysing certain error statistics as well as by comparison with the random walk time series prediction method. The predictions up to five days in advance are satisfactorily done in this manner.en_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJMS Vol.49(08) [August 2020]en_US
dc.subjectCurrent observationsen_US
dc.subjectCurrent predictionen_US
dc.subjectModel treeen_US
dc.subjectNumerical ocean modelen_US
dc.subjectOcean currentsen_US
dc.titleImproving numerical current prediction with Model Treeen_US
Appears in Collections:IJMS Vol.49(08) [August 2020]

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