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IITM Journal of Information Technology

ISSN (P) 2395-5457 | Single Blind Peer Reviewed Journal

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INSTITUTE OF INNOVATION IN TECHNOLOGY & MANAGEMENT
Affiliated to GGSIPU, NAAC Grade ‘A’, ISO 14001:2015, 17020:2012, 21001:2018 & 50001:2018 Certified,
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AI for Disaster Reporting: A Review of Technical Barriers and Ethical Imperatives.

Sunaina, Rahul Rishi, Yogesh Kumar
Department of Computer Science and Engineering, MDU Rohtak, Haryana, India

Abstract: With increasing frequency of natural disasters brought about by climate change and high rate of urbanization, there has been an urgency in having efficient and real-time response mechanisms. The given review paper analyzes the disruptive nature of Artificial Intelligence (AI) and new computing capabilities, such as the Internet of Things (IoT), Big Data, and Unmanned Aerial Vehicles (UAVs) in the evolution of disaster reporting and management. We examine how machine learning, deep learning, Natural Language Processing (NLP), and computer vision can be used to provide rapid situational awareness, detect disasters in real-time and assess damage automatically using multi-source data through social media, sensors and remote sensing. This paper examines the use of AI in various disaster stages, such as mitigation, preparedness, response and recovery, with a particular emphasis on improvement of decision making and resource reallocation using specific applications of AI, such as, AI-driven triage and drone-based geohazard surveillance. Moreover, we comment on the combination of high-fidelity Digital Twins and Large Language Models (LLMs) to streamline the evacuation planning and information distribution. Although the impact is huge in the realm of response effectiveness, we recognize severe technical and ethical obstacles, such as the problem of data reliability, misinformation, bias in algorithms, and privacy. The paper ends by providing a road map to future research which focuses on creation of scalable, glorifiable, and integrative AI-based frameworks to enhance disaster resilience globally.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, NLP, Computer Vision, IoT, Big Data, UAVs, Remote Sensing, Situational Awareness, Damage Assessment, GIS.

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