Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55314
Title: Improving numerical current prediction with Model Tree
Authors: Dauji, S
Deo, M C
Keywords: Current observations;Current prediction;Model tree;Numerical ocean model;Ocean currents
Issue Date: Aug-2020
Publisher: NISCAIR-CSIR, India
Abstract: A 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.
Page(s): 1350-1358
URI: http://nopr.niscair.res.in/handle/123456789/55314
ISSN: 2582-6727 (Online); 2582-6506 (Print)
Appears in Collections:IJMS Vol.49(08) [August 2020]

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