WP5 discussion
From Intamap
Objectives of work package 5.1
Automatic mapping algorithms for environmental monitoring systems are based on statistical techniques that provide essential information to exclude technical anomalies when triggering early warnings. Given the large consequences of such early warnings, it is essential that false alarms get minimized as much as possible. It is the purpose of this WP to define the conditions that would trigger warnings and alarms in an optimal way in the frame of INTAMAP and to design various scenarios and case studies for the testing of the algorithms and interfaces.
Early warning systems: Identifying anomalies with monitoring networks
The first task in this workpackage is to describe all environmental scenarios (e.g. nuclear accident, local anomalies) that would generate a response of the monitoring network that is different from the normality. Different from the normality should be understood as any situation in which particular attention would be required from the network managers. Each scenario should be explained and the possible responses of the network documented. The means (e.g. automatic emails, visual/acoustical signals in the user interfaces) to report these anomalies will also be presented.
- Description of environmental scenarios (e.g. nuclear accident, local anomalies) that would generate a response of the monitoring network and would trigger early warnings.
- Each scenario will be explained and the possible responses of the network documented.
- The means (e.g. automatic emails, visual/acoustical signals in the user interfaces) to report these anomalies will also be presented.
The work package has been extended to introduce the measurement techniques used in the networks and to describe the strategies of both, networks and prognosis systems.
II. The German gamma dose rate network
III. Description of the new data logger
IV. The In situ gamma spectrometric systems
V. Description of early warning procedures in the German gamma dose rate network
VI. The means to report anomalies
