Ung S T, Williams V, Bonsall S, Wang J
Marine, Offshore and Transport Research Group, School of Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK.
J Safety Res. 2006;37(3):245-60. doi: 10.1016/j.jsr.2006.02.002. Epub 2006 Jul 3.
The traditional fuzzy-rule-based risk assessment technique has been applied in many industries due to the capability of combining different parameters to obtain an overall risk. However, a drawback occurs as the technique is applied in circumstances where there are multiple parameters to be evaluated that are described by multiple linguistic terms.
In this study, a risk prediction model incorporating fuzzy set theory and Artificial Neural Network (ANN) capable of resolving the problem encountered is proposed. An algorithm capable of converting the risk-related parameters and the overall risk level from the fuzzy property to the crisp-valued attribute is also developed. Its application is demonstrated by a test case evaluating the navigational safety within port areas.
It is concluded that a risk predicting ANN model is capable of generating reliable results as long as the training data takes into account any potential circumstance that may be met.
This paper provides safety assessment practitioners with a novel and flexible framework of modelling risks using a fuzzy-rule-base technique. It is especially applicable in circumstances where there are multiple parameters to be considered. The proposed framework also enables the port industry to manage navigational safety in a rational manner.
基于模糊规则的传统风险评估技术已在许多行业中得到应用，因为它能够结合不同参数以获得总体风险。然而，当该技术应用于存在多个需要评估的参数且这些参数由多个语言术语描述的情况时，就会出现一个缺点。
在本研究中，提出了一种结合模糊集理论和人工神经网络（ANN）的风险预测模型，该模型能够解决所遇到的问题。还开发了一种算法，能够将与风险相关的参数和总体风险水平从模糊属性转换为清晰值属性。通过一个评估港口区域内航行安全的测试案例展示了其应用。
得出的结论是，只要训练数据考虑到可能遇到的任何潜在情况，风险预测人工神经网络模型就能够产生可靠的结果。
本文为安全评估从业者提供了一个使用基于模糊规则的技术进行风险建模的新颖且灵活的框架。它特别适用于存在多个参数需要考虑的情况。所提出的框架还使港口行业能够以合理的方式管理航行安全。
