Test case based risk predictions using artificial neural network.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Ung
S T
ST
Marine, Offshore and Transport Research Group, School of Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK.
Williams
V
V
Bonsall
S
S
Wang
J
J
D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D008037:Linguistics; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012449:Safety; D012767:Ships; D012815:Signal Processing, Computer-Assisted