A WeVoS-CBR Approach to Oil Spill Problem

  • Emilio Corchado
  • Bruno Baruque
  • Aitor Mata
  • Juan M. Corchado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5271)


The hybrid intelligent system presented here, forecasts the presence or not of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. The proposed CBR includes a novel network for data classification and data retrieval. Such network works as a summarization algorithm for the results of an ensemble of Visualization Induced Self-Organizing Maps. This algorithm, called Weighted Voting Superposition (WeVoS), is mainly aimed to achieve the lowest topographic error in the map. The system uses information obtained from various satellites such as salinity, temperature, pressure, number and area of the slicks. WeVoS-CBR system has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.


Case-Based Reasoning Oil Spill Topology Preserving Maps Ensemble summarization Self Organizing Memory RBF 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Watson, I.: Case-Based Reasoning Is a Methodology Not a Technology. Knowledge-Based Systems 12, 303–308 (1999)CrossRefGoogle Scholar
  2. 2.
    Baruque, B., Corchado, E.: WeVoS: A Topology Preserving Ensemble Summarization Algorithm. Data Mining and Knowledge Discovery (2008)Google Scholar
  3. 3.
    Palenzuela, J.M.T., Vilas, L.G., Cuadrado, M.S.: Use of Asar Images to Study the Evolution of the Prestige Oil Spill Off the Galician Coast. International Journal of Remote Sensing 27, 1931–1950 (2006)CrossRefGoogle Scholar
  4. 4.
    Solberg, A.H.S., Storvik, G., Solberg, R., Volden, E.: Automatic Detection of Oil Spills in Ers Sar Images. IEEE Transactions on Geoscience and Remote Sensing 37, 1916–1924 (1999)CrossRefGoogle Scholar
  5. 5.
    Aamodt, A.: A Knowledge-Intensive, Integrated Approach to Problem Solving and Sustained Learning. Knowledge Engineering and Image Processing Group. University of Trondheim (1991)Google Scholar
  6. 6.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7, 39–59 (1994)Google Scholar
  7. 7.
    Kohonen, T.: The Self-Organizing Map. Neurocomputing 21, 1–6 (1998)zbMATHCrossRefGoogle Scholar
  8. 8.
    Yin, H.: Data Visualisation and Manifold Mapping Using the Visom. Neural Networks 15, 1005–1016 (2002)CrossRefGoogle Scholar
  9. 9.
    Baruque, B., Corchado, E., Yin, H.: Visom Ensembles for Visualization and Classification. In: Sandoval, F., Gonzalez Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 235–243. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Corchado, E., Baruque, B., Yin, H.: Boosting Unsupervised Competitive Learning Ensembles. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 339–348. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Corchado, J.M., Fdez-Riverola, F.: Fsfrt: Forecasting System for Red Tides. Applied Intelligence 21, 251–264 (2004)CrossRefGoogle Scholar
  12. 12.
    Karayiannis, N.B., Mi, G.W.: Growing Radial Basis Neural Networks: Merging Supervised Andunsupervised Learning with Network Growth Techniques. IEEE Transactions on Neural Networks 8, 1492–1506 (1997)CrossRefGoogle Scholar
  13. 13.
    Sørmo, F., Cassens, J., Aamodt, A.: Explanation in Case-Based Reasoning–Perspectives and Goals. Artificial Intelligence Review 24, 109–143 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Emilio Corchado
    • 1
  • Bruno Baruque
    • 2
  • Aitor Mata
    • 2
  • Juan M. Corchado
    • 2
  1. 1.University of BurgosSpain
  2. 2.University of SalamancaSpain

Personalised recommendations