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

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 widen...

Full description

Saved in:
Bibliographic Details
Published inAutomatisierungstechnik : AT Vol. 72; no. 6; pp. 564 - 578
Main Authors Budde, Matthias, Hilbring, Desiree, Vogl, Jonathan, Dittmar, Daniel, Abecker, Andreas
Format Journal Article
LanguageEnglish
Published De Gruyter 25.06.2024
Subjects
business & location intelligence
disy Cadenza
environmental sensor data management & analytics
FROST
geoAI software infrastructure
geoAI-Software-Infrastruktur
server
Umweltdatenmanagement und -analyse
water quality monitoring
Überwachung der Wasserqualität
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0178-2312
2196-677X
DOI:10.1515/auto-2024-0034