A Multi-Source Data-Driven Natural Disaster Emergency Management System: Risk Early Warning Framework, Social Media Opinion Mining, and Monitoring System Development

Abstract

In response to the systemic lag and reactive difficulties present in traditional natural disaster management models regarding the timeliness of risk perception, the accuracy of decision-making, and the coordination of emergency responses, this paper constructs a natural disaster risk early warning system framework that spans the entire cycle of pre-disaster prevention, mid-disaster emergency response, and post-disaster recovery, forming a closed-loop governance chain of “monitoring–assessment–release–response–feedback–optimization.” On this basis, taking the “8·21” wildfire in Beibei, Chongqing, China, in 2022 as a typical case, a social media public opinion visualization analysis was conducted based on 5,507 text posts from the Weibo platform. Through word frequency statistics, temporal comment volume analysis, and word cloud visualization, the study reveals the spatially structured characteristics of public attention and the temporal evolution pattern of sentiment tendencies.  Finally, from four dimensions—improving classified monitoring and graded early warning systems, establishing information sharing and hierarchical reporting systems, perfecting early warning release and graded response mechanisms, and advancing the informatization of monitoring, forecasting, and early warning efforts—the paper proposes a development path for a comprehensive natural disaster monitoring and early warning system, aiming to provide theoretical references and practical guidance for advancing the modernization of natural disaster prevention and emergency management capabilities.