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Integrating Artificial Intelligence and IoT in Earthquake Disaster Management: A Comparative Literature Review

Yashaswini A, Skandana KV

Abstract


The integration of AI and IoT has revolutionized the management of earthquake disasters and redefined methods of prediction, preparation, response, and recovery. This paper presents a comprehensive literature survey and comparison of 11 research studies exploring the integration of the latest IoT and AI in the management of seismic disasters. The analysis examines technological methods, applications, efficiency, and research gaps. The key results present the advancements in the machine learning (ML) models including CNN, LSTM, hybrid frameworks, graph neural networks (GNNs), and the real-time data acquisition made possible by IoT with seismic sensors, UAVs, and GNSS stations. These innovations have highly enhanced the early warning system (EEWS), damage assessment, and risk analysis. These advancements notwithstanding, data availability, real-time processing, scalability, and lack of standards are the areas where there is a lack of standard communication protocols for IoT systems.

 This paper outlines key research gaps, which include the requirement of longitudinal data for vulnerability assessments and the development of scalable generalized systems applicable to different geological regions.

Also advocates for interdisciplinary cooperation in order to address these challenges and make the system more reliable. This study helps in the outlining of possible future research areas, as it focuses on the change potential of AI and IoT in reducing the earthquake impact and in enhancing disaster resilience.

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References


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