Machine learning -based decision support framework for CBRN protection

Tamás Kegyes ^1,2, Zoltán Süle ², János Abonyi ^1,2

⁰HUN-REN-PE Complex Systems Monitoring Research Group, Egyetem utca 10., Veszprém, 8200, Hungary.

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February 26, 2024

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Summary

This study emphasizes predictive analytics for enhancing chemical, biological, radiological, and nuclear (CBRN) defense. It outlines modern CBRN protection technologies and information management strategies for improved incident detection and response.

Area Of Science

Interdisciplinary science focusing on chemical, biological, radiological, and nuclear (CBRN) threat detection and protection.
Integration of expertise from chemistry, physics, meteorology, military strategy, programming, and data science.

Background

Detecting CBRN incidents is a critical, long-standing research priority.
Advancements in technology, data processing, and automation are transforming CBRN defense capabilities.
CBRN protection is a complex field requiring collaboration across multiple scientific and operational domains.

Purpose Of The Study

To highlight the significance of predictive analytics in modern CBRN defense.
To provide an overview of key components in contemporary CBRN protection technologies.
To summarize conceptual requirements for CBRN reconnaissance and decision support.

Main Methods

Review of current CBRN defense technologies and their components.
Analysis of conceptual requirements for CBRN reconnaissance and decision support.
Examination of the role and opportunities of information management in CBRN processes.

Main Results

Identification of predictive analytics as crucial for enhancing CBRN defense.
Overview of essential elements within modern CBRN defense systems.
Summary of strategic needs for effective CBRN reconnaissance and decision-making.

Conclusions

Continuous, structured development is key to advancing CBRN defense capabilities.
Predictive analytics and robust information management are vital for improving CBRN incident response.
The interdisciplinary nature of CBRN protection necessitates integrated approaches.

Keywords:

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