Machine learning -based decision support framework for CBRN protection

Abstract

Detecting chemical, biological, radiological and nuclear (CBRN) incidents is a high priority task and has been a topic of intensive research for decades. Ongoing technological, data processing, and automation developments are opening up new potentials in CBRN protection, which has become a complex, interdisciplinary field of science. According to it, chemists, physicists, meteorologists, military experts, programmers, and data scientists are all involved in the research. The key to effectively enhancing CBRN defence capabilities is continuous and targeted development along a well-structured concept. Our study highlights the importance of predictive analytics by providing an overview of the main components of modern CBRN defence technologies, including a summary of the conceptual requirements for CBRN reconnaissance and decision support steps, and by presenting the role and recent opportunities of information management in these processes.

Keywords

• CBRN protection framework
• Machine learning
• Predictive analytics
• Information management
• Decision support system