Abstract
Short term current prediction for operational purposes is commonly carried out with the help of numerical ocean circulation models. The numerical models have advantage that they are based on the physics of the underlying process. However because of their spatial nature they may not be so accurate while making station-specific predictions. In such cases data-driven approaches like artificial neural network (ANN)’s trained on the basis of location-specific data may work better. In this paper an attempt is made to do daily predictions of ocean currents by combination of a numerical model and ANNs. The difference in the current velocity estimated by the numerical model and actual observations at a given time was calculated and corresponding error time series was formed based on all past numerical estimations and observations. An ANN was trained over such time series to predict errors for future, which were added to the numerical estimation so as to predict daily current velocities over multiple days in future. The suggested approach, implemented at two locations in Indian Ocean, was found to perform satisfactory current predictions up to a lead time of 5 days, as ascertained through various error statistics. The standalone networks once trained using the numerical outcome can reproduce such output well over future time without using variety of data and computational resources required for running the numerical model on a continuous basis.
Highlights
Short term current prediction for operational purposes is commonly carried out with the help of numericalocean circulation models
The operational prediction of ocean currents is necessary for carrying out a variety of activities such as shipping and towing as well as search and rescue, tracking pollutants and oil spill, monitoring coastal water quality, forecasting power output from current energy farms, and, issuing warnings to fishermen and to organizers of sports and swimming events
The network thereafter carried out the time series forecasting and predicted errors over multiple time steps in future, which were added to the numerical estimation and predictions of currents over desired time steps in future were produced
Summary
Short term current prediction for operational purposes is commonly carried out with the help of numericalocean circulation models. The numerical models have advantage that they are based on the physics of the underlying process. Because of their spatial nature they may not be so accurate while making stationspecific predictions. In such cases data-driven approaches like artificial neural network (ANN)’s trained on the basis of location-specific data may work better. The performance of a numerical current model can be enhanced by various means It can be done in timeindependent or offline mode as in the optimal interpolation approach or in time-dependent or online mode as in variational methods and Kalman Filter. The last approach has advantages like simplicity, less requirement of data in general and it is suitable for site-specific predictions (Jain and Deo 2006)
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