Applied Intelligence

, Volume 21, Issue 3, pp 251–264 | Cite as

FSfRT: Forecasting System for Red Tides

  • Florentino Fdez-Riverola
  • Juan M. Corchado


A hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used at a different stage of the reasoning cycle of the case-based reasoning system to retrieve historical data, to adapt it to the present problem and to review the proposed solution. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments, in which the system operated in a real environment, are presented.


Radial Basis Function Iberian Peninsula Fuzzy Model Forecast System Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. Tomczak and J.S. Godfrey, Regional Oceanographic: An Introduction, Pergamon: New York, 1994.Google Scholar
  2. 2.
    E. Fernandez, “Las Mareas Rojas en las Rías Gallegas,” Technical Report, Departamento de Ecología y Biología Animal, Universidad de Vigo, 1998.Google Scholar
  3. 3.
    J.M. Corchado and C. Fyfe. “Unsupervised neural network for temperature forecasting,” Artificial Intelligence in Engineering, vol. 13, no. 4, pp. 351–357, 1999.Google Scholar
  4. 4.
    J.M. Corchado and B. Lees, “A hybrid case-based model for forecasting,” Applied Artificial Intelligence, vol. 15, no. 2, pp. 105–127, 2001.Google Scholar
  5. 5.
    J.M. Corchado, B. Lees, and J. Aiken, “Hybrid instance-based system for predicting ocean temperatures,” International Journal of Computational Intelligence and Applications, vol. 1, no. 1, pp. 35–52, 2001.Google Scholar
  6. 6.
    J.M. Corchado, J. Aiken, and N. Rees, Artificial Intelligence Models for Oceanographic Forecasting, Plymouth Marine Laboratory: UK, 2001.Google Scholar
  7. 7.
    I.Watson, Applying Case-based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann: San Mateo, CA, 1997.Google Scholar
  8. 8.
    R.C. Schank, Dynamic Memory, Cambridge University Press: Cambridge, UK, 1982.Google Scholar
  9. 9.
    G. Klein and L. Whitaker, “Using analogues to predict and plan,” in Proc. Workshop Case-Based Reasoning, 1988, pp. 224–232.Google Scholar
  10. 10.
    J. Kolodner, Case-based Reasoning, Morgan Kaufmann: San Mateo, CA, 1993.Google Scholar
  11. 11.
    S.K. Pal, T.S. Dilon, and D.S. Yeung, “Soft computing in case based reasoning,” International Journal of Intelligent Systems, Springer-Verlag: London, 2000.Google Scholar
  12. 12.
    C.K. Riesbeck and R.C. Schank, Inside Case-Based Reasoning, Lawrence Elrlbaum Ass: Hillsdale, 1989.Google Scholar
  13. 13.
    A. Aamodt and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” AICOM, vol. 7, pp. 39–59, 1994.Google Scholar
  14. 14.
    G. Nakhaeizadeh, “Learning prediction of time series. A theoretical and empirical comparison of CBR with some other approaches,” in Topics in Case-Based Reasoning, First European Workshop, EWCBR'93, edited by S. Wess, K.D. Althoff, and M.M.Y. Richter, Kaiserslautern, Springer: Berlin, 1994.Google Scholar
  15. 15.
    G.G. Lendaris and A.M. Fraser, “Visual fitting and extrapolation,” Time Series Prediction, Forecasting the Future and Understanding the Past, edited by A.S. Weigend and N.A. Gershenfield, Addison Wesley, 1994, pp. 35–46.Google Scholar
  16. 16.
    G.P. Lekkas, N.M. Arouris, and L.L. Viras, “Case-based reasoning in environmental monitoring applications,” Artificial Intelligence, no. 8, pp. 349–376, 1994.Google Scholar
  17. 17.
    H.S. Mcintyre, D.D. Achabal, and C.M. Miller, “Applying casebased reasoning to forecasting retail sales,” Journal of Retailing, vol. 69, no. 4, pp. 372–398, 1993.Google Scholar
  18. 18.
    R.H. Stottler, “Case-based reasoning for cost and sales prediction,” AI Expert, pp. 25–33, 1994.Google Scholar
  19. 19.
    J.M. Corchado, B. Lees, C. Fyfe, N. Ress, and J. Aiken, “Neuro-adaptation method for a case based reasoning system,” in IJCNN'98, Alaska, 1998, pp. 304–312.Google Scholar
  20. 20.
    J.M. Corchado and B. Lees, “Adaptation of cases for case-based forecasting with neural network support,” in Soft Computing in Case Based Reasoning, edited by S.K. Pal, T.S. Dilon, and D.S. Yeung, Springer-Verlag: London, 2000.Google Scholar
  21. 21.
    S. Fraga, D.M. Anderson, I. Bravo, B. Reguera, K.A. Steidinger, and C.M. Yetsch, “Influence of upwelling relaxation on dinoflagellates and shellfish toxity in Ria de Vigo,” Est. Coast and Shelf Sci., no. 27, pp. 349–361, 1988.Google Scholar
  22. 22.
    G.M. Hallegraeff, “A review of harmful algal blooms and their apparent global increase,” Phycologia, no. 32, pp. 79–99, 1993.Google Scholar
  23. 23.
    D.M. Anderson, “Toxic algal blooms and red tides:Aglobal perspective,” in RedTides: Biology Environmental Science and Toxicology, edited by T. Okaichi, D.M. Anderson, and T. Nemoto, Elsevier: New York, 1989, pp. 11–16.Google Scholar
  24. 24.
    D. Kamykowski, “The simulation of a southern California red tide using characteristics of a simultaneously-measured internal wave field,” Ecol. Model., vol. 12, pp. 253–265, 1981.Google Scholar
  25. 25.
    M. Watanabe and A. Harashima, “Interaction between motile phytoplankton and Langmuir circulation,” Ecol. Model., vol. 31, pp. 175–183, 1986.Google Scholar
  26. 26.
    P.J.S. Franks and D.M. Anderson, “Toxic phytoplankton blooms in the southwestern Gulf of Maine: Testing hypotheses of physical control using historical data,” Marine Biology, vol. 112, pp. 165–174, 1992.Google Scholar
  27. 27.
    B. Fritzke, “Growing self-organizing networks-why?,” in ESANN'96, Brussels, 1996, pp. 61–72.Google Scholar
  28. 28.
    B. Fritzke, “Fast learning with incremental RBF networks,” Neural Processing Letters, vol. 1, no. 1, pp. 2–5, 1994.Google Scholar
  29. 29.
    Y. Jin, W. von Seelen, and B. Sendhoff, “Extracting interpretable fuzzy rules from RBF neural networks,” Technical Report, Institut für Neuroinformatik. Ruhr-Universität Bochum, Jan. 2000.Google Scholar
  30. 30.
    B. Fritzke, “Growing cell structures-A self-organizing network for unsupervised and supervised learning,” Technical Report, International Computer Science Institute, Berkeley, 1993.Google Scholar
  31. 31.
    T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 15, pp. 116–132, 1985.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Florentino Fdez-Riverola
    • 1
  • Juan M. Corchado
    • 2
  1. 1.Dpto. de Informática, E.S.E.I.University of Vigo Edificio Politécnico, Campus Universitario As Lagoas, s/n.OurenseSpain
  2. 2.Dpto. de Informática y Automática, Facultad de CienciasUniversity of SalamancaSalamancaSpain

Personalised recommendations