AI-Driven Prediction and Resilience Building in Tourism Risk and Crisis Management

Abstract

Tourism systems are exposed to health, environmental, geopolitical, technological, and operational disruptions. This conceptual study explains how natural language processing, knowledge graphs, and time-series forecasting can be combined to strengthen early warning and organizational resilience. It uses a structured integrative review and mechanism synthesis rather than presenting original empirical estimates. The resulting framework connects heterogeneous data inputs to signal detection, relational diagnosis, forecasting, human validation, coordinated response, and post-event learning. Three propositions specify when artificial intelligence is most likely to improve preparedness, supply-chain adaptability, and accountable emergency decisions. The analysis also identifies limits involving data drift, rare events, multilingual bias, privacy, cybersecurity, and automation bias. The paper contributes an auditable human-in-the-loop architecture that treats artificial intelligence as decision support rather than an autonomous crisis manager