Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1234-1237· 0 citations· 13 references
Abstract
Climate-related disasters in island regions are increasing due to climate change and geographical characteristics. Jeju Island is particularly vulnerable to marine, coastal, and mountainous disasters such as coastal accidents, low-salinity inflow, flash floods, and localized heavy rainfall. Existing disaster management systems mainly rely on post-event monitoring and manual decision-making processes, making it difficult to provide rapid responses and predictive actions. This paper proposes an Edge AI-based Disaster AX platform architecture for Island Regions. The proposed architecture integrates Edge AI CCTV systems, environmental sensors, disaster prediction models, and an AI-based integrated control platform to support real-time monitoring, prediction, and early response. The architecture is designed to process data at edge devices while utilizing cloud-based integrated analysis for large-scale decision support. The proposed approach aims to reduce disaster response time and improve operational efficiency in island environments.
Flooding is one of the most pervasive and destructive natural hazards, with its frequency and intensity expected to worsen under climate change. While advances in geospatial analytics, Internet of Things infrastructures, and artificial intelligence have enhanced urban data ecosystems, existing smart city platforms rema...
Sk Tahsin Hossain, Tan Yigitcanlar, Zhao-Hui Lin et al.· Natural Hazards· 0 citations
Natural disasters cause significant human, economic, and infrastructural losses worldwide, yet traditional disaster response systems often rely on static risk assessments and isolated data sources that fail to capture evolving severity, spatial dependencies, and real-time operational constraints such as infrastructure...
S. B. Priya, S. K. Sanju, S. Priyanga et al.· IEEE Access· 0 citations
A comprehensive, Artificial Intelligence-driven framework for disaster management is proposed, which synthesizes state-of-the-art computational models with foundational hazard theories and culminates in a translational, highly scalable AI framework engineered for equitable, real-world deployment.
Risav Dey· International Journal For Mu...· 0 citations
This study proposes a smart ice-prevention framework for the Yellow River based on distributed sensing networks, digital twin technology, and multi-source information fusion that provides an effective methodology for large-scale monitoring systems, environmental sensing networks, and data-driven hazard management in co...
W. Du, L.-L. Li· Advanced Electromagnetics· 0 citations
As climate change, rapid urbanization, and inadequate drainage infrastructure continue to increase the frequency and severity of urban flooding, intelligent and reliable flood prediction systems have become essential for minimizing disaster impacts and improving urban resilience. This study presents a novel Proposed Hy...
R. J., L. D, M. V et al.· 2026 International Conferenc...· 0 citations
Disasters triggered by hydro-meteorological, geophysical, and climatic hazards are increasing in frequency and severity, straining the capacity of conventional disaster risk management (DRM) systems that rely on manual data collection and delayed decision-making [1], [5]. Recent advances in Artificial Intelligence (AI)...
Chirag S. Patel· Journal of Commerce, Economi...· 0 citations
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