European legislation expects operators of critical infrastructures to make greater efforts to safeguard their economically significant infrastructures. This includes the assessment and evaluation of potential risks that could arise in the event of damage and disruptions. The risk assessment must not be content with the assumption of a failure-free operation but must keep the potential consequences in mind.
Both the threat from external influences on critical infrastructures and the goods transported within them are subject to higher risks, which can be better assessed using available data. Artificial Intelligence (AI) can contribute to decision support through pattern recognition and self-learning algorithms. In advance, this leads to improved fact-based awareness, which can enable short-term response measures in the event of damage incidents. New in this context is the concept of GeoAI, which makes the aspect of geographical spatial reference accessible to artificial intelligence. Organizations can integrate location analysis and machine learning to drive innovations, make predictions, and gain real-time insights faster. illustrate examples of how this can be achieved in the context of pipeline infrastructure using public available data.
The report aims to illustrate possibilities using examples of how this can be achieved in the context of pipeline infrastructure with available data in order to conduct a quantitative risk assessment.