A Monte Carlo simulation based two-stage adaptive resonance theory mapping approach for offshore oil spill vulnerability index classification

Pu Li, Bing Chen, Zelin Li, Xiao Zheng, Hongjing Wu, Liang Jing, Kenneth Lee
Marine Pollution Bulletin 2014 September 15, 86 (1-2): 434-42
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.


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