<![CDATA[This work fills a major theoretical gap in tourist resilience: the systemic imbalance between cognitive processes and physical infrastructure, which increases susceptibility during hydrometeorological crises. Existing frameworks fail to explain why locations with similar hazard exposure display substantial outcome disparities, as seen by Venice's lengthy flood disruption against Singapore's predictive mitigation success. The study makes two major theoretical contributions: the Resilience Engineering Framework (REF), which combines cognitive load theory, behavioral intelligence, and AI-mediated feedback loops to model systemic brittleness; and the Adaptive Design Protocol (ADP), which applies REF principles to spatial, governance, and infrastructural interventions. The study takes a sequential mixed-methods approach, with (1) big data analytics across 20 destinations quantifying cognitive stressors (e.g., decision fatigue amplifying evacuation errors by 22%), (2) stakeholder surveys identifying governance misalignments, and (3) agent-based modeling validating REF dynamics. Empirical results show that ADP implementation reduces rebound time by 41% and infrastructure damage costs by 37% through metamorphic adaptation, as demonstrated by Bali's AI-driven crowd-flow systems, which speed up recovery by 58% through cognitive load optimization. The findings demonstrate that shifting fragility into anticipatory capacity necessitates cognitively grounded design, providing a reproducible approach for regenerative tourist ecosystems.]]>
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