• Addison, P. S., 2002: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance. Institute of Physics Publishing, 353 pp.

    • Search Google Scholar
    • Export Citation
  • Aguilar-Martinez, S., and Hsieh W. W. , 2009: Forecasts of tropical Pacific sea surface temperatures by neural networks and support vector regression. Int. J. Oceanogr., 2009, 167239, doi:10.1155/2009/167239.

    • Search Google Scholar
    • Export Citation
  • Alexandridis, A. K., and Zapranis A. D. , 2013: Wavelet neural networks: A practical guide. Neural Networks, 42, 127, doi:10.1016/j.neunet.2013.01.008.

    • Search Google Scholar
    • Export Citation
  • ASCE Task Committee, 2000a: Artificial neural networks in hydrology. I: Preliminary concepts. J. Hydrol. Eng., 5, 115123, doi:10.1061/(ASCE)1084-0699(2000)5:2(115).

    • Search Google Scholar
    • Export Citation
  • ASCE Task Committee, 2000b: Artificial neural networks in hydrology. II: Hydrologic applications. J. Hydrol. Eng., 5, 124137, doi:10.1061/(ASCE)1084-0699(2000)5:2(124).

    • Search Google Scholar
    • Export Citation
  • Collins, D. C., Reason C. J. C. , and Tangang F. , 2004: Predictability of Indian Ocean sea surface temperature using canonical correlation analysis. Climate Dyn., 22, 481497, doi:10.1007/s00382-004-0390-4.

    • Search Google Scholar
    • Export Citation
  • Deka, P. C., and Prahlada R. , 2012: Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time. Ocean Eng., 43, 3242, doi:10.1016/j.oceaneng.2012.01.017.

    • Search Google Scholar
    • Export Citation
  • Dixit, P., Londhe S. , and Dandawate Y. , 2015: Removing prediction lag in wave height forecasting using neurowavelet modelling technique. Ocean Eng., 93, 7483, doi:10.1016/j.oceaneng.2014.10.009.

    • Search Google Scholar
    • Export Citation
  • Garcia-Gorriz, E., and Garcia-Sanchez J. , 2007: Prediction of sea surface temperatures in the western Mediterranean Sea by neural networks using satellite observations. Geophys. Res. Lett., 34, L11603, doi:10.1029/2007GL029888.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. M., and Malmgren B. A. , 2009: Comparison of the accuracy of SSTA estimates by artificial neural networks (ANN) and other quantitative methods using radiolarian data from the Antarctic and Pacific Oceans. Earth Sci. India, 2, 5275.

    • Search Google Scholar
    • Export Citation
  • Hagan, M. T., Demuth H. B. , Beale M. H. , and De Jesùs O. , 2014: Neural Network Design. 2nd ed. Hagan and Demuth, 1012 pp.

  • Haykin, S., 1999: Adaptive filters. 6 pp. [Available online at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.42.6386&rep=rep1&type=pdf.]

    • Search Google Scholar
    • Export Citation
  • Jain, P., and Deo M. C. , 2006: Neural networks in ocean engineering. Ships Offshore Struct., 1, 2535, doi:10.1533/saos.2004.0005.

  • Kug, J.-S., Kang I.-S. , Lee J.-Y. , and Jhun J.-G. , 2004: A statistical approach to Indian Ocean sea surface temperature prediction using a dynamical ENSO prediction. Geophys. Res. Lett., 31, L09212, doi:10.1029/2003GL019209.

    • Search Google Scholar
    • Export Citation
  • Lee, Y.-H., Ho C.-R. , Su F.-C. , Kuo N.-J. , and Cheng Y.-H. , 2011: The use of neural networks in identifying error sources in satellite-derived tropical SST estimates. Sensors, 11, 75307544, doi:10.3390/s110807530.

    • Search Google Scholar
    • Export Citation
  • Mahongo, S. B., and Deo M. C. , 2013: Using artificial neural networks to forecast monthly and seasonal sea surface temperature anomalies in the western Indian Ocean. Int. J. Ocean Climate Syst., 4, 133150, doi:10.1260/1759-3131.4.2.133.

    • Search Google Scholar
    • Export Citation
  • Maier, H. R., and Dandy G. C. , 2000: Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environ. Modell. Software, 15, 101124, doi:10.1016/S1364-8152(99)00007-9.

    • Search Google Scholar
    • Export Citation
  • Mallat, S. G., 1998: A Wavelet Tour of Signal Processing. Academic Press, 577 pp.

  • Moraud, E. M., 2009: Wavelet networks. School of Informatics, University of Edinburgh, 8 pp. [Available online at http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0809/martinmoraud.pdf.]

  • Neetu, Sharma R. , Basu S. , Sarkar A. , and Pal P. K. , 2011: Data-adaptive prediction of sea-surface temperature in the Arabian Sea. IEEE Geosci. Remote Sens. Lett., 8, 913, doi:10.1109/LGRS.2010.2050674.

    • Search Google Scholar
    • Export Citation
  • Patil, K. R., Deo M. C. , and Ravichandran M. , 2014: Neural networks to predict sea surface temperature. Proc. 19th Int. Conf. on Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014), Bhopal, Madhya Pradesh, India, Indian Society of Hydraulics, 13171326.

  • Pozzi, M., Malmgren B. A. , and Monechi S. , 2000: Sea surface-water temperature and isotopic reconstructions from nannoplankton data using artificial neural networks. Palaeontol. Electron., 3, 4. [Available online at http://palaeo-electronica.org/2000_2/neural/issue2_00.htm.]

    • Search Google Scholar
    • Export Citation
  • Shoaib, M., Shamseldin A. Y. , and Melville B. W. , 2014: Comparative study of different wavelet based neural network models for rainfall–runoff modeling. J. Hydrol., 515, 4758, doi:10.1016/j.jhydrol.2014.04.055.

    • Search Google Scholar
    • Export Citation
  • Tang, B., Hsieh W. W. , Monahan A. H. , and Tangang F. T. , 2000: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J. Climate, 13, 287293, doi:10.1175/1520-0442(2000)013<0287:SCBNNA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tangang, F. T., Hsieh W. W. , and Tang B. , 1997: Forecasting the equatorial Pacific sea surface temperatures by neural network models. Climate Dyn., 13, 135147, doi:10.1007/s003820050156.

    • Search Google Scholar
    • Export Citation
  • Tanvir, M. S., and Mujtaba I. M. , 2006: Neural network based correlations for estimating temperature elevation for seawater in MSF desalination process. Desalination, 195, 251272, doi:10.1016/j.desal.2005.11.013.

    • Search Google Scholar
    • Export Citation
  • Tripathi, K. C., Rai S. , Pandey A. C. , and Das I. M. L. , 2008: Southern Indian Ocean SST indices as early predictors of Indian summer monsoon. Indian J. Mar. Sci., 38, 7076.

    • Search Google Scholar
    • Export Citation
  • Wasserman, P. D., 1993: Advanced Methods in Neural Computing. John Wiley & Sons, Inc., 255 pp.

  • Wu, A., Hsieh W. W. , and Tang B. , 2006: Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Networks, 19, 145154, doi:10.1016/j.neunet.2006.01.004.

    • Search Google Scholar
    • Export Citation
  • Wu, K. K., 1994: Neural Networks and Simulation Methods. Marcel Decker, 456 pp.

  • Xue, Y., and Leetmaa A. , 2000: Forecasts of tropical Pacific SST and sea level using a Markov model. Geophys. Res. Lett., 27, 27012704, doi:10.1029/1999GL011107.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 22 22 22
PDF Downloads 17 17 17

Prediction of Sea Surface Temperature by Combining Numerical and Neural Techniques

View More View Less
  • 1 Indian Institute of Technology Bombay, Mumbai, India
  • | 2 Indian National Centre for Ocean Information Services, Earth System Science Organisation, Hyderabad, India
Restricted access

Abstract

The prediction of sea surface temperature (SST) in real-time or online mode has applications in planning marine operations and forecasting climate. This paper demonstrates how SST measurements can be combined with numerical estimations with the help of neural networks and how reliable site-specific forecasts can be made accordingly. Additionally, this work demonstrates the skill of a special wavelet neural network in this task. The study was conducted at six different locations in the Indian Ocean and over three time scales (daily, weekly, and monthly). At every time step, the difference between the numerical estimation and the SST measurement was evaluated, an error time series was formed, and errors over future time steps were forecasted. The time series forecasting was affected through neural networks. The predicted errors were added to the numerical estimation, and SST predictions were made over five time steps in the future. The performance of this procedure was assessed through various error statistics, which showed a highly satisfactory functioning of this scheme. The wavelet neural network based on the particular basic or mother wavelet called the “Meyer wavelet with discrete approximation” worked more satisfactorily than other wavelets.

Corresponding author address: M. C. Deo, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India. E-mail: mcdeo@civil.iitb.ac.in

Abstract

The prediction of sea surface temperature (SST) in real-time or online mode has applications in planning marine operations and forecasting climate. This paper demonstrates how SST measurements can be combined with numerical estimations with the help of neural networks and how reliable site-specific forecasts can be made accordingly. Additionally, this work demonstrates the skill of a special wavelet neural network in this task. The study was conducted at six different locations in the Indian Ocean and over three time scales (daily, weekly, and monthly). At every time step, the difference between the numerical estimation and the SST measurement was evaluated, an error time series was formed, and errors over future time steps were forecasted. The time series forecasting was affected through neural networks. The predicted errors were added to the numerical estimation, and SST predictions were made over five time steps in the future. The performance of this procedure was assessed through various error statistics, which showed a highly satisfactory functioning of this scheme. The wavelet neural network based on the particular basic or mother wavelet called the “Meyer wavelet with discrete approximation” worked more satisfactorily than other wavelets.

Corresponding author address: M. C. Deo, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India. E-mail: mcdeo@civil.iitb.ac.in
Save