DOI: 10.1016/j.marpolbul.2014.06.036
Scopus记录号: 2-s2.0-84922675802
论文题名: A Monte Carlo simulation based two-stage adaptive resonance theory mapping approach for offshore oil spill vulnerability index classification
作者: Li P. ; Chen B. ; Li Z. ; Zheng X. ; Wu H. ; Jing L. ; Lee K.
刊名: Marine Pollution Bulletin
ISSN: 0025-326X
EISSN: 1879-3363
出版年: 2014
卷: 86, 期: 2018-01-02 起始页码: 434
结束页码: 442
语种: 英语
英文关键词: Adaptive resonance theory
; Classification
; Monte carlo simulation
; Offshore oil spill
; Oil spill vulnerability index
; Uncertainty
Scopus关键词: Algorithms
; Budget control
; Classification (of information)
; Intelligent systems
; Mapping
; Oil shale
; Oil spills
; Resonance
; Adaptive resonance theory
; Classification process
; Classification results
; Multiple features
; Offshore oil
; Uncertainty
; Uncertainty and complexity
; Vulnerability index
; Monte Carlo methods
; Article
; environmental management
; environmental monitoring
; environmental parameters
; Monte Carlo method
; oil spill
; oil spill vulnerability index
; risk assessment
; seashore
; statistical analysis
; two stage adaptive resonance theory mapping
; analysis
; computer simulation
; geographic mapping
; Monte Carlo method
; oil spill
; prevention and control
; procedures
; statistics and numerical data
; theoretical model
; uncertainty
; Computer Simulation
; Geographic Mapping
; Models, Theoretical
; Monte Carlo Method
; Petroleum Pollution
; Risk Assessment
; Uncertainty
Scopus学科分类: Agricultural and Biological Sciences: Aquatic Science
; Earth and Planetary Sciences: Oceanography
; Environmental Science: Pollution
英文摘要: In this paper, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) model was developed to classify a given site into distinguished zones representing different levels of offshore Oil Spill Vulnerability Index (OSVI). It consisted of an adaptive resonance theory (ART) module, an ART Mapping module, and a centroid determination module. Monte Carlo simulation was integrated with the TSAM approach to address uncertainties that widely exist in site conditions. The applicability of the proposed model was validated by classifying a large coastal area, which was surrounded by potential oil spill sources, based on 12 features. Statistical analysis of the results indicated that the classification process was affected by multiple features instead of one single feature. The classification results also provided the least or desired number of zones which can sufficiently represent the levels of offshore OSVI in an area under uncertainty and complexity, saving time and budget in spill monitoring and response. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/85711
Appears in Collections: 过去全球变化的重建 全球变化的国际研究计划
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作者单位: Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada; Wealth from Oceans National Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australian Resources Research Centre, Kensington, WA, Australia
Recommended Citation:
Li P.,Chen B.,Li Z.,et al. A Monte Carlo simulation based two-stage adaptive resonance theory mapping approach for offshore oil spill vulnerability index classification[J]. Marine Pollution Bulletin,2014-01-01,86(2018-01-02)