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Solving the Oil Spill Problem Using a Combination of CBR and a Summarization of SOM Ensembles

  • Aitor Mata
  • Emilio Corchado
  • Bruno Baruque
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 50)

Abstract

In this paper, a forecasting system is presented. It predicts the presence of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. CBR systems are designed to generate solutions to a certain problem by analysing historical data where previous solutions are stored. The system explained includes a novel network for data classification and retrieval. Such network works as a summarization algorithm for the results of an ensemble of Self-Organizing Maps. This algorithm, called Weighted Voting Superposition (WeVoS), is aimed to achieve the lowest topographic error in the map. The WeVoS-CBR system has been able to precisely predict the presence of oil slicks in the open sea areas of the north west of the Galician coast.

Keywords

Case-Based Reasoning oil spill Self Organizing Memory summarization Radial Basis Function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

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

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