From Sensors to Society: Rethinking Disaster Early Warning Systems in the Age of AI

Abstract

Increased frequency and severity of natural disasters underscore the urgent need for effective early warning systems (EWS) as a key component of disaster risk management. This narrative review explores the technological, social, and policy dimensions that shape EWS effectiveness, with a focus on the integration of artificial intelligence, Internet of Things, and satellite monitoring. A systematic literature search was conducted using Scopus, Google Scholar, and PubMed, employing Boolean operators to capture studies addressing technology, accessibility, and public trust. Findings reveal that while recent technological innovations have significantly improved hazard detection and response times, persistent barriers remain in terms of infrastructure quality, digital access, and institutional credibility, particularly in low-resource settings. The review highlights how national policies, such as those implemented in the United States, Philippines, and Ethiopia, contribute to more resilient EWS frameworks when aligned with local engagement and cross-sectoral collaboration. Community-based early warning models, education initiatives, and inclusive communication strategies emerge as critical success factors in enhancing public responsiveness and trust. The review concludes that effective EWS must extend beyond technical sophistication to incorporate social equity, participatory governance, and long-term adaptability. Future research should prioritize the exploration of digital inequality and the ethical application of advanced technologies. Integrating community trust and accessibility as core pillars is essential to build inclusive systems capable of protecting the most vulnerable from disaster risks.