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dc.contributor.authorAparna, S.G.
dc.contributor.authorDeSouza, S.
dc.contributor.authorArjun, N.B.
dc.date.accessioned18/2/2019
dc.date.accessioned2019-02-18T20:16:33Z
dc.date.available18/6/2018
dc.date.available2019-02-18T20:16:33Z
dc.date.issued2018
dc.identifier.citationInternational Journal of Remote Sensing, vol.39(12); 2018; 4214-4231
dc.identifier.issn0143-1161
dc.identifier.urihttps://doi.org/10.1080/01431161.2018.1454623
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/7905
dc.description.abstractWe present an artificial neural network model to predict the sea surface temperature (SST) and delineate SST fronts in the northe-astern Arabian Sea. The predictions are made one day in advance, using current days SST for predicting the SST of the next day. The model is used to predict the SST map for every single day during 20132015. The results show that more than 75% of the time the model error is �0.5oC. For the years 2014 and 2015, 80% of the predictions had an error �0.5oC. The model performance is dependent on the availability of data during the previous days. Thus during the summer monsoon months, when the data availability is comparatively less, the errors in the prediction are slightly higher. The model is also able to capture SST fronts.
dc.languageen
dc.publisherTaylor & Francis
dc.relation.isreferencedbySCI
dc.titlePrediction of daily sea surface temperature using artificial neural networks
dc.typeJournal Article
dc.affiliation.authorCSIR-National Institute of Oceanography, Dona Paula, Goa, India


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