A forecasting solution to the oil spill problem based on a hybrid intelligent system

被引：60

作者：

Baruque, Bruno ^{[2
]}

Corchado, Emilio ^{[1
]}

Mata, Aitor ^{[1
]}

Corchado, Juan M. ^{[1
]}

机构：

[1] Univ Salamanca, Dept Informat & Automat, E-37008 Salamanca, Spain

[2] Univ Burgos, Dept Civil Engn, Burgos 09006, Spain

来源：

INFORMATION SCIENCES | 2010年 / 180卷 / 10期

关键词：

Case-Based Reasoning; Oil spill; Self organizing memory; Radial Basis Function; Ensembles; Fusion algorithms; CONTINGENCY; MANAGEMENT; NETWORK; SUPPORT; STRAIT; DRIFT;

D O I：

10.1016/j.ins.2009.12.032

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

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 topo-graphically 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. (C) 2010 Elsevier Inc. All rights reserved.

引用

页码：2029 / 2043

页数：15

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