A Systemic Approach for Early Warning in Crisis Prevention and Management

  • Achim Kuwertz
  • Maximilian Moll
  • Jennifer SanderEmail author
  • Stefan Pickl
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)


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.


Early warning Expert knowledge models Deep Learning 



Inputs to the paper from Marian Sorin Nistor are gratefully acknowledged.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Achim Kuwertz
    • 1
  • Maximilian Moll
    • 2
  • Jennifer Sander
    • 1
    Email author
  • Stefan Pickl
    • 2
  1. 1.Fraunhofer IOSB, Institute of Optronics, System Technologies and Image ExploitationKarlsruheGermany
  2. 2.Universität der Bundeswehr MünchenNeubibergGermany

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