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dc.contributor.author | Baruque, Bruno | |
dc.contributor.author | Corchado Rodríguez, Emilio Santiago | |
dc.contributor.author | Mata Conde, Aitor | |
dc.contributor.author | Corchado Rodríguez, Juan Manuel | |
dc.date.accessioned | 2017-09-05T11:02:04Z | |
dc.date.available | 2017-09-05T11:02:04Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Information Sciences. Volumen 180 (10), pp. 2029-2043. Elsevier BV. | |
dc.identifier.issn | 0020-0255 (Print) | |
dc.identifier.uri | http://hdl.handle.net/10366/134419 | |
dc.description.abstract | Oil spills represent one of the most destructive environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be critical in reducing environmental risks. The system presented here uses the Case-Based Reasoning (CBR) methodology to forecast the presence or absence of oil slicks in certain open sea areas after an oil spill. CBR is a computational methodology designed to generate solutions to certain problems by analysing previous solutions given to previously solved problems. The proposed CBR system includes a novel network for data classification and retrieval. This type of network, which is constructed by using an algorithm to summarize the results of an ensemble of Self-Organizing Maps, is explained and analysed in the present study. The Weighted Voting Superposition (WeVoS) algorithm mainly aims to achieve the best topographically ordered representation of a dataset in the map. This study shows how the proposed system, called WeVoS-CBR, uses information such as salinity, temperature, pressure, number and area of the slicks, obtained from various satellites to accurately predict the presence of oil slicks in the north-west of the Galician coast, using historical data. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ | |
dc.subject | Computer Science | |
dc.title | A forecasting solution to the oil spill problem based on a hybrid intelligent system | |
dc.type | info:eu-repo/semantics/article | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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