Community-Based Earthquake Early Warning and Pattern Recurrence Prediction

Abstract

This study explores the development of community-based earthquake early warning (EEW) systems integrated with pattern recurrence prediction as an innovative approach to disaster risk reduction in seismically active regions like the Philippines. Unlike traditional centralized seismic monitoring, community-based systems utilize distributed low-cost sensors and smartphone technologies to enhance real-time detection and localized responsiveness. The incorporation of machine learning and statistical models further enables the identification of recurring seismic patterns, improving both detection accuracy and predictive capabilities. While challenges such as data standardization, connectivity, and public engagement persist, these systems promote inclusive participation and resilience at the community level. The study highlights the importance of combining technological advancements with policy support and public education to maximize the effectiveness of EEW systems. Overall, the integration of decentralized warning systems and predictive analytics offers a scalable and adaptive solution for minimizing earthquake-related risks.