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dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorMata Conde, Aitor
dc.identifier.citationAdvances in Case-Based Reasoning Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5239, pp. 573-586.
dc.identifier.isbn978-3-540-85501-9 (Print) / 978-3-540-85502-6 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.description.abstractA new predicting system is presented in which the aim is to forecast the presence or not of oil slicks in a certain area of the open sea after an oil spill. In this case, the CBR methodology has been chosen to solve the problem. The system designed to predict the presence of oil slicks wraps other artificial intelligence techniques such as a Growing Radial Basis Function Networks, Growing Cell Structures and Fast Iterative Kernel Principal Components Analysis in order to develop the different phases of the CBR cycle. The proposed system uses information such as sea salinity, sea temperature, wind, currents, pressure, number and area of the slicks.... obtained from various satellites. The system has been trained using data obtained after the Prestige accident. Oil Spill CBR system (OSCBR) has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectComputer Science
dc.titlePredicting the Presence of Oil Slicks After an Oil Spill

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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported