AI-Driven Big Data for Real-Time Disaster Response
DOI:
https://doi.org/10.5281/g8q2cw23Keywords:
Big Data; Disaster; Streaming Data; Real-Time; Predictive Modelling; Resource Allocation; Command-and-Control; Decision Management; Social and Open Data; Sensor Networks; Internet of Things.Abstract
AI-based big data systems support near real-time disaster response and resource allocation. These systems shorten reaction times by leveraging scarce resources (data, bandwidth, processing, and decision-making) more efficiently in disaster management. A layered architecture provides distributed computing, data fusion, and decision management for near real-time deployment. Future implementations will prototype alarm generation using social-media and sensor data.
Large-scale disasters place severe and often insurmountable demands on emergency agencies. Under such conditions, resource allocation plays a critical role in minimizing disaster impact, but conventional methods based on historical data often cannot meet real-time requirements. The integration of geographical information with data from social networks, sensor networks, and other sources can facilitate the relevant prediction and decision-making processes, but the available data, computing, and human resources are all severely constrained—with the same scarcity of responses and alerts as for other data types. For these reasons, proposed methods shorten response times by applying AI-based big-data systems.
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