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Commonalities and Differences in ML-Pipelines for Air Quality Systems

In: Advances and New Trends in Environmental Informatics 2023

Author

Listed:
  • Cezary Orlowski

    (WSB Merito University Gdansk)

  • Grit Behrens

    (HSBI/Bielefeld University of Applied Sciences)

  • Kostas Karatzas

    (Aristotle University of Thessaloniki)

Abstract

This paper compares three ML-pipelines in Air Quality (AQ) Systems, namely a fog layer management model for IoT-systems, a low-cost AQ sensor system with sensor calibration and data fusion competences and a ML-method research based on low-cost OpenSensorMap. The three ML-pipelines are described, commonalities and differences worked out and the advantages of every technique are led over in an effort of a combined ML-pipeline which could be realised in a scientific cooperation of the three groups.

Suggested Citation

  • Cezary Orlowski & Grit Behrens & Kostas Karatzas, 2024. "Commonalities and Differences in ML-Pipelines for Air Quality Systems," Progress in IS, in: Volker Wohlgemuth & Dieter Kranzlmüller & Maximilian Höb (ed.), Advances and New Trends in Environmental Informatics 2023, pages 21-37, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-46902-2_2
    DOI: 10.1007/978-3-031-46902-2_2
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