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dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorBaruque, Bruno
dc.contributor.authorMata Conde, Aitor
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2017-09-06T09:15:37Z
dc.date.available2017-09-06T09:15:37Z
dc.date.issued2008
dc.identifier.citationHybrid Artificial Intelligence Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5271, pp. 378-384.
dc.identifier.isbn978-3-540-87655-7 (Print) / 978-3-540-87656-4 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135002
dc.description.abstractThe 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleA WeVoS-CBR Approach to Oil Spill Problem
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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