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A Systemic Approach for Early Warning in Crisis Prevention and Management

: Kuwertz, Achim; Moll, Maximilian; Sander, Jennifer; Pickl, Stefan


Ahram, Tareq (Ed.):
Human systems engineering and design II : Proceedings of the 2nd International Conference on Human Systems Engineering and Design (IHSED2019); Future Trends and Applications, September 16-18, 2019, Universität der Bundeswehr München, Munich, Germany
Cham: Springer Nature, 2020 (Advances in Intelligent Systems and Computing 1026)
ISBN: 978-3-030-27927-1 (Print)
ISBN: 978-3-030-27928-8 (Online)
International Conference on Human Systems Engineering and Design (IHSED) <2, 2019, München>
Fraunhofer IOSB ()
early warning; expert knowledge models; deep learning

Given the importance of early warning in crisis prevention this paper discusses both knowledge-based and data-driven approaches. Traditional knowledge-based methods are often of limited suitability for use in crisis prevention and management, since they typically use a model which has been designed in advance. Novel data-driven Artificial Intelligence (AI) methods such as Deep Learning demonstrate promising skills to learn implicitly from data alone, but require significant computing capacities and a large amount of annotated, high-quality training data. This paper addresses research results on concepts and methods that may serve as building blocks for realizing a decision support tool based on hybrid AI methods, which combine knowledge-based and data-driven methods in a dynamic way and provide an adaptable solution to mitigate the downsides of each individual approach.