A Computer System for CBRN Contamination Threats Analysis Support, Prediction Their Effects and Alarming the Population: Polish Case Study

Institute of Computer and Information Systems, Faculty of Cybernetics, Military University of Technology, Warsaw, Poland

^a Corresponding author: zbigniew.tarapata@wat.edu.pl

Abstract

The article outlines a concept for the system supporting analyses of threats related to contamination and alarming (WAZkA), for the purpose of the National System for Detection of Contamination and Alarming (Krajowy System Wykrywania Skażeń i Alarmowania, KSWSiA) in Poland. Additionally, the article presents the point of designing such system, its concept and components. The main objective of WAZkA is to support the following processes of the KSWSiA: information exchange between the system elements and coordination of the system operations, as well as to prepare assessment and expert analyses, needed by the decision-making bodies, with respect to risk emergency situations during natural disasters, technical failures or other events resulting in biological, chemical or radioactive contamination. The selected modules included in the WAZkA system: Event Tree Analyzer, visualization module (COP) and emulators of the threats monitoring systems, were described as well as the idea of using the system for the purpose of training, including the designed emulators of the risk monitoring systems together with the scenario editor. The Event Tree Anlyzer is a graphical representation of a chronological sequence of events, significant from the point of view of the functioning of the object, which occur after a given event that initiates such sequence. COP is the GIS tool, which allows to present an operating situation on the basis of background maps. The application offers a possibility of enriching the presented operating situation with the risk data provided in the form of NATO ADat-P3 (CBRN) reports. Emulators of the threats monitoring systems allow to generate, in an artificial and user-controlled manner, data on risks, which “pretend” to be real data obtained from the monitoring systems. The above approach significantly facilitates the organization of training and learning about rare situations or situations that have never occurred, but that are potentially dangerous.