Ocean Dynamics

, Volume 67, Issue 3–4, pp 357–368 | Cite as

Prediction of daily sea surface temperature using efficient neural networks

  • Kalpesh Patil
  • Makaranad Chintamani Deo


Short-term prediction of sea surface temperature (SST) is commonly achieved through numerical models. Numerical approaches are more suitable for use over a large spatial domain than in a specific site because of the difficulties involved in resolving various physical sub-processes at local levels. Therefore, for a given location, a data-driven approach such as neural networks may provide a better alternative. The application of neural networks, however, needs a large experimentation in their architecture, training methods, and formation of appropriate input–output pairs. A network trained in this manner can provide more attractive results if the advances in network architecture are additionally considered. With this in mind, we propose the use of wavelet neural networks (WNNs) for prediction of daily SST values. The prediction of daily SST values was carried out using WNN over 5 days into the future at six different locations in the Indian Ocean. First, the accuracy of site-specific SST values predicted by a numerical model, ROMS, was assessed against the in situ records. The result pointed out the necessity for alternative approaches. First, traditional networks were tried and after noticing their poor performance, WNN was used. This approach produced attractive forecasts when judged through various error statistics. When all locations were viewed together, the mean absolute error was within 0.18 to 0.32 °C for a 5-day-ahead forecast. The WNN approach was thus found to add value to the numerical method of SST prediction when location-specific information is desired.


Sea surface temperature SST prediction Neural networks Wavelet networks 



This study was made as part of a research project (no. 13MES001) funded by ESSO-INCOIS, Ministry of Earth Sciences, Government of India, Hyderabad, India, under the “High resolution operational ocean forecast and reanalysis system (HOOFS)” program. The authors gratefully acknowledge the help of Dr. Francis P. A. and Dr. M. Ravichandran, INCOIS, Hyderabad, in releasing numerical model-based data and for helpful suggestions in implementing the research project. Special thanks are due to Mrs. Anuradha Modi and Mr. K Kaviyazhahu from INCOIS for their help in compiling and providing the ROMS data.


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© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Indian Institute of Technology BombayMumbaiIndia

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