Natural and human-induced disasters are increasing in frequency and severity due to climate change, rapid urbanization, environmental degradation, and population growth. Conventional disaster prediction methods often lack the speed and accuracy needed for real-time emergency response. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics enable intelligent systems to analyze diverse real-time data from satellites, IoT sensors, weather stations, seismic networks, GIS, and social media for accurate disaster forecasting. This paper presents an AI-based decision support framework integrating data acquisition, preprocessing, feature engineering, machine learning, deep learning, and automated decision-making within a scalable cloud-edge architecture. The study also reviews existing AI-based disaster prediction approaches, identifies their limitations, and compares their performance. The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.
Alan Bundy, Karen Spärck Jones· International Journal of Mod...· 0 citations
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional Edge computing has emerged as a transformative extension of cloud computing by addressing the limitations of latency, bandwidth, and scalability in real-time applications. With the increasing demand for ultra-low latency in autonomous vehicles, industrial automation, telemedicine, smart cities, and augmented reality, traditional cloud architectures face challenges due to centralized processing and network delays. Edge computing overcomes these issues by processing data closer to end devices, enabling faster decision-making and reduced communication overhead. This paper presents a comprehensive survey of edge computing architectures for ultra-low latency applications, covering key technologies such as 5G, Software-Defined Networking (SDN), Network Function Virtualization (NFV), Artificial Intelligence (AI), microservices, and container orchestration. It also examines major challenges, including resource management, interoperability, security, and energy efficiency. A multi-layer edge computing framework with intelligent task scheduling and dynamic resource allocation is proposed to optimize latency and resource utilization. Experimental findings demonstrate significant improvements over conventional cloud architectures, achieving over 70% reduction in end-to-end latency and 65% improvement in resource efficiency. The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations