A new CBR approach to the oil spill problem

Oil spills represent one of the most destructing environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be crucial in order to reduce the environmental risks. The 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. CBR is a computational methodology designed to generate solutions to a certain problem by analysing previous solutions given to previous solved problems. The proposed system wraps other artificial intelligence techniques such as a Radial Basis Function Networks, Growing Cell Structures and Principal Components Analysis in order to develop the different phases of the CBR cycle. CBR systems have never been used before to solve oil slicks problems. The proposed system uses information obtained from various satellites such as salinity, temperature, pressure, number and area of the slicks... OSCBR system has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.

[1]  S. V. N. Vishwanathan,et al.  Fast Iterative Kernel Principal Component Analysis , 2007, J. Mach. Learn. Res..

[2]  J. M. Torres Palenzuela,et al.  Use of ASAR images to study the evolution of the Prestige oil spill off the Galician coast , 2006 .

[3]  Rune Solberg,et al.  Automatic detection of oil spills in ERS SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

[4]  Francisco Azuaje,et al.  Discovering relevance knowledge in data: a growing cell structures approach , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[5]  K. Nittis,et al.  BUILDING THE EUROPEAN CAPACITY IN OPERATIONAL OCEANOGRAPHY , 2003 .

[6]  Iphigenia Keramitsoglou,et al.  Decision Support System for Managing Oil Spill Events , 2003, Environmental management.

[7]  Igor Brovchenko,et al.  THE MODELING SYSTEM FOR SIMULATION OF THE OIL SPILLS IN THE BLACK SEA , 2003 .

[8]  M. T. Babu,et al.  Trajectory of an oil spill off Goa, eastern Arabian Sea: field observations and simulations. , 2007, Environmental pollution.

[9]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[10]  Bonifacio Martín del Brío,et al.  Redes neuronales y sistemas borrosos: introducción teórica y práctica , 1997 .

[11]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[12]  Thomas Roth-Berghofer,et al.  Goals and Kinds of Explanations in Case-Based Reasoning , 2005, Wissensmanagement.

[13]  Juan M. Corchado,et al.  FSfRT: Forecasting System for Red Tides , 2004, Applied Intelligence.

[14]  Christos Douligeris,et al.  Development of OSIMS: an Oil Spill Information Management System , 1995 .

[15]  Marco Pintore,et al.  Automatic design of growing radial basis function neural networks based on neighboorhood concepts , 2007 .

[16]  J. M. Price,et al.  Evaluation of an oil spill trajectory model using satellite-tracked, oil-spill-simulating drifters , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

[17]  Agnar Aamodt,et al.  Explanation in Case-Based Reasoning–Perspectives and Goals , 2005, Artificial Intelligence Review.

[18]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[19]  Nicolaos B. Karayiannis,et al.  Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques , 1997, IEEE Trans. Neural Networks.