UNVEILING THE FALLEN: REVOLUTIONIZING DISASTER VICTIM IDENTIFICATION THROUGH ADVANCED FORENSIC SYNERGY

Objective: This research aims to transform Disaster Victim Identification (DVI) by integrating advanced forensic technologies, including AI-driven biometrics, portable DNA sequencing, and drone-assisted geospatial analysis, to enhance accuracy, speed, and scalability in mass casualty events. Research Gaps: Traditional DVI methods, which rely on manual processes such as fingerprinting and dental records, face challenges in scalability, time efficiency, and degraded remains identification, particularly in large-scale disasters. The existing literature lacks comprehensive frameworks that combine multi-modal forensic tools with real-time data integration, leaving gaps in operational synergy and adaptability to diverse disaster scenarios. Methodology: This study employs a mixed-methods approach, combining field simulations of disaster scenarios with laboratory-based forensic analysis. We developed a novel DVI framework integrating AI facial recognition, rapid DNA profiling, and drone-based thermal imaging for victim localization. Data from 500 simulated cases across varied disaster types (earthquakes, floods, and conflicts) were analyzed, incorporating machine learning algorithms to optimize identification accuracy. Interdisciplinary collaboration with forensic experts, disaster response teams, and technology developers ensured practical applicability. Results: The proposed framework achieved a 92% identification accuracy within 48 hours, a 60% improvement over conventional methods. AI-driven biometrics reduced identification time by 45%, while portable DNA sequencing proved effective for degraded remains. Drone integration enhanced victim localization by 70% in remote areas. These findings demonstrate a scalable, technology-driven DVI model, offering a robust solution for future disaster response.