Genetic fuzzy system for automating maritime risk assessment

Abstract

This chapter uses genetic fuzzy systems (GFS) to assess the risk level of maritime vessels transmitting Automatic Identification System (AIS) data. Previous risk assessment approaches based on fuzzy inference systems (FIS) relied on domain experts to specify the FIS membership functions as well as the fuzzy rule base (FRB), a burdensome and time-consuming process. This chapter aims to alleviate this burden by learning the membership functions and FRB for the FIS of an existing Risk Management Framework (RMF) directly from data. The proposed methodology is tested with four different case studies in maritime risk analysis. Each case study concerns a unique scenario involving a particular region: the Gulf of Guinea, the Strait of Malacca, the Northern Atlantic during a storm, and the Northern Atlantic during a period of calm seas. The experiments compare 14 GFS algorithms from the KEEL software package and evaluate the resulting FRBs according to their accuracy and interpretability. The results indicate that IVTURS, LogitBoost, and NSLV generate the most accurate rule bases while SGERD, GCCL, NSLV, and GBML each generate interpretable rule bases. Finally, IVTURS, NSLV, and GBML algorithms offer a reasonable compromise between accuracy and interpretability.