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NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain

  • Matthias Budde

    Dr.-Ing. Matthias Budde received both a graduate degree and a Ph.D. in Computer Science from Karlsruhe Institute of Technology (KIT). Since 2020, he has been working in research and innovation at Disy Informationssysteme GmbH. His past research topics include environmental sensing/AI, mobile computing, internet of things (IoT), and human-computer-interaction (HCI).

     EMAIL logo     , Desiree Hilbring

    Dr.-Ing. Desiree Hilbring has a degree in Geodesy. In her research at Fraunhofer IOSB, she dealt with system architectures based on open geodata standards, which have been applied for the development of information systems in the environmental sector or in crisis management. In recent years, the main focus has been on the application and integration of AI in corresponding systems.

         , Jonathan Vogl

    Jonathan Vogl received a M. Sc. in Mathematics from Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. His research interests include environment-related topics in computer science, in particular on data-driven approaches, as well as systems and software engineering.

         , Daniel Dittmar

    Daniel Dittmar received a M.Sc. in Mathematics from Karlsruhe Institut of Technologie (KIT), Karlsruhe, Germany in 2017. He has since been passionate about software development, especially focusing on spatial data processing and visualization.

         and Andreas Abecker

    Dr. rer. pol. Dipl.-Inform. Andreas Abecker has been working with the German Research Center for Artificial Intelligence (DFKI Kaiserslautern) and the FZI Research Center for Information Technologies (Karlsruhe). Since 2010, he is Head of Innovation Management at Disy Informationssysteme GmbH. His research topics include GeoAI, Big Spatial Data and AI for environment informatics.
Published/Copyright: June 25, 2024
at - Automatisierungstechnik
From the journal at - Automatisierungstechnik Volume 72 Issue 6

Abstract

In the realm of environmental governance, civil servants confront a plethora of diverse datasets, including time series, geospatial vector data, and raster data. However, unlocking the transformative potential of Artificial Intelligence (AI) models to analyze this data poses the challenge of a widening technical proficiency gap in public administration. This paper explores the intersection of expanding environmental datasets and advanced analytics. Through a real-world project lens, our work aims to guide public administration entities, fostering seamless integration of AI-driven analytics and data-driven decision-making. We present a modular technical architecture that proposes pragmatic solutions that have the potential to empower civil servants. This approach contributes to accelerating environmental governance into an era of more informed and efficient, data-driven practices.

Zusammenfassung

Im Bereich der Umweltüberwachung sehen sich Verwaltungen zunehmend mit einer Fülle unterschiedlicher Datensätze konfrontiert, darunter Zeitreihen und räumliche Vektor- und Rasterdaten. Die Erschließung des transformativen Potenzials von Modellen der Künstlichen Intelligenz (KI) zur Analyse dieser Daten stellt jedoch eine Herausforderung dar, da sich die technische Kompetenzlücke in der öffentlichen Verwaltung vergrößert. In diesem Beitrag wird die Schnittstelle zwischen wachsenden Umweltdatensätzen und fortschrittlicher Analytik untersucht. Die vorliegende Arbeit zielt darauf ab, die öffentliche Verwaltung zu befähigen und die nahtlose Integration von KI-gestützter Analyse und datengestützter Entscheidungsfindung zu fördern. Wir präsentieren eine modulare technische Architektur, die pragmatische Lösungen präsentiert, die das Potenzial haben, KI in der Verwaltung einfacher nutzbar zu machen. Dieser Ansatz trägt dazu bei, die Umweltüberwachung einen Schritt in eine Ära informierterer und effizienterer, datengesteuerter Praktiken zu führen.

Keywords: environmental sensor data management & analytics; water quality monitoring; business & location intelligence; disy Cadenza; FROST® server; geoAI software infrastructure
Schlagwörter: Umweltdatenmanagement und -analyse; Überwachung der Wasserqualität; Business & Location Intelligence; disy Cadenza; FROST®-Server; geoAI-Software-Infrastruktur
Corresponding author: Matthias Budde, Disy Informationssysteme GmbH, Ludwig-Erhardt-Allee 6, 76131 Karlsruhe, Germany, E-mail:

About the authors

Matthias Budde

Dr.-Ing. Matthias Budde received both a graduate degree and a Ph.D. in Computer Science from Karlsruhe Institute of Technology (KIT). Since 2020, he has been working in research and innovation at Disy Informationssysteme GmbH. His past research topics include environmental sensing/AI, mobile computing, internet of things (IoT), and human-computer-interaction (HCI).

Desiree Hilbring

Dr.-Ing. Desiree Hilbring has a degree in Geodesy. In her research at Fraunhofer IOSB, she dealt with system architectures based on open geodata standards, which have been applied for the development of information systems in the environmental sector or in crisis management. In recent years, the main focus has been on the application and integration of AI in corresponding systems.

Jonathan Vogl

Jonathan Vogl received a M. Sc. in Mathematics from Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. His research interests include environment-related topics in computer science, in particular on data-driven approaches, as well as systems and software engineering.

Daniel Dittmar

Daniel Dittmar received a M.Sc. in Mathematics from Karlsruhe Institut of Technologie (KIT), Karlsruhe, Germany in 2017. He has since been passionate about software development, especially focusing on spatial data processing and visualization.

Andreas Abecker

Dr. rer. pol. Dipl.-Inform. Andreas Abecker has been working with the German Research Center for Artificial Intelligence (DFKI Kaiserslautern) and the FZI Research Center for Information Technologies (Karlsruhe). Since 2010, he is Head of Innovation Management at Disy Informationssysteme GmbH. His research topics include GeoAI, Big Spatial Data and AI for environment informatics.

Acknowledgments

The NiMo 4.0 consortium wishes to thank all organizations that provided data as a basis for both AI research as well as system development.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: This work was partially funded by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) as part of the program “KI-Leuchttüurme” [43], project “NiMo 4.0” [6] (grant number 67KI2048).

  5. Data availability: Not applicable.

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Received: 2024-02-18
Accepted: 2024-04-17
Published Online: 2024-06-25
Published in Print: 2024-06-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Advanced water management
  4. Survey
  5. Advances in dissolved oxygen prediction and control methods in aquaculture: a review
  6. Methods
  7. Probabilistic multi-step ahead streamflow forecast based on deep learning
  8. A new assessment method on co-occurring mountain and plain floods based on copula functions
  9. Application
  10. Prescriptive analytics for low and high water management in German waterways
  11. Tools
  12. Integration of selected AI methods into a simulation tool for urban wastewater systems – towards practical application
  13. NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain
https://doi.org/10.1515/auto-2024-0034
Keywords for this article
environmental sensor data management & analytics; water quality monitoring; business & location intelligence; disy Cadenza; FROST® server; geoAI software infrastructure

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Advanced water management
  4. Survey
  5. Advances in dissolved oxygen prediction and control methods in aquaculture: a review
  6. Methods
  7. Probabilistic multi-step ahead streamflow forecast based on deep learning
  8. A new assessment method on co-occurring mountain and plain floods based on copula functions
  9. Application
  10. Prescriptive analytics for low and high water management in German waterways
  11. Tools
  12. Integration of selected AI methods into a simulation tool for urban wastewater systems – towards practical application
  13. NiMo 4.0 – Enabling advanced data analytics with AI for environmental governance in the water domain
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