• Ravinder Singh Singh

Abstract • Unplanned explosions at munition facilities pose significant threats to human life, national security, and the environment. Effective explosive hazard mitigation is crucial to prevent catastrophic consequences. This research aims to develop an advanced artificial intelligence (AI) framework for predictive risk assessment and proactive reduction at munition sites. • Background and significance: Munition facilities are critical infrastructure for national defence, but they also pose significant risks. Estimated losses range from $201.9 billion to $403.8 billion, with potential fatalities ranging from 26,910 to 53,820. Current risk assessment methodologies are limited by reactive strategies, incomplete data, simplistic models, human error, and lack of real-time monitoring. • Objective: Develop an advanced AI framework leveraging machine learning algorithms, predictive analytics, and real-time monitoring to predict potential risks and provide proactive recommendations for risk reduction. • Methodology: The proposed framework integrates historical incident reports, sensor data and environmental data with advanced machine learning algorithms to predict explosive risks. Predictive analytics identify potential hazards and prioritize risk reduction measures. Real-time monitoring enables prompt response to emerging risks. • Expected outcomes: Enhanced safety, efficiency, and reduced risks at munition facilities. The AI-powered framework optimizes hazard mitigation, ensuring proactive risk assessment and reduction capabilities. This research contributes to the development of advanced AI solutions for explosive hazard mitigation, enhancing national security and protecting human life.

optizatio, AI, Risk Assessment, Risk Mitigation, UEMS

"OPTIMIZATION OF EXPLOSIVE HAZARD MITIGATION VIA DEVELOPMENT OF AN ADVANCED ARTIFICIAL INTELLIGENCE FRAMEWORK FOR PREDICTIVE RISK ASSESSMENT AND PROACTIVE REDUCTION AT MUNITION FACILITIES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.e264-e271, January-2025, Available :http://www.jetir.org/papers/JETIR2501434.pdf

"OPTIMIZATION OF EXPLOSIVE HAZARD MITIGATION VIA DEVELOPMENT OF AN ADVANCED ARTIFICIAL INTELLIGENCE FRAMEWORK FOR PREDICTIVE RISK ASSESSMENT AND PROACTIVE REDUCTION AT MUNITION FACILITIES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppe264-e271, January-2025, Available at : http://www.jetir.org/papers/JETIR2501434.pdf