文章基本信息

标题：A combined numerical and neural technique for short term prediction of ocean currents in the Indian Ocean
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作者：Dauji Saha ; Dauji Saha ; M. C. Deo 等
期刊名称：Environmental Systems Research
电子版ISSN：2193-2697
出版年度：2016
卷号：5
期号：1
页码：1-14
DOI：10.1186/s40068-016-0057-2
语种：English
出版社：Springer
摘要：Abstract Background 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. Results 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. Conclusions 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.
关键词：KeywordsEnNumerical current modelsArtificial neural networkCurrent predictionOcean currentsCurrent observations