A Conservative Hybrid Risk Assessment Model for Navigational Obstacles Integrating Fuzzy Logic with a Qualitative Matrix and a Red Flag Protocol

Navigational obstacles pose compound collision and pollution risks, yet conventional quantitative assessment models relying on data-driven “best-estimate” approaches suffer from “alarm masking”, whereby critical risk signals are diluted through averaging. This study develops a conservative hybrid risk assessment framework that preserves critical risk signals while systematically incorporating qualitative factors beyond the reach of quantitative data. The fuzzy inference rules of an existing integrated model were redesigned into a priority-stratified hybrid hierarchical–parallel fuzzy inference system (HHP-FIS); a qualitative evaluation matrix of four categories and 32 items was constructed through a two-stage expert procedure (a Delphi panel of eight officials and an analytic hierarchy process (AHP) survey of 59 experts with 34 valid responses); and a Red Flag Protocol was introduced as a fail-safe veto mechanism. The framework was verified through eighteen paired random-input simulations across two grid systems and a case study of a 68.9-ton drifting fishing vessel near Seongsan Port, Jeju Island. The model upwardly reclassified underestimated low-frequency, high-consequence scenarios, raised the case-study risk from Low (44.6 and 47.7) to Moderate (59.1 and 74.8), with an action level consistent with expert judgment, and was robust to rule-weight perturbations, providing a decision-support tool for obstacle-removal prioritization and marine pollution prevention.