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AI-Powered Monitoring for the Ecological Health of Eckernförde Bay

Artificial intelligence is poised to revolutionize the monitoring of Eckernförde Bay's ecological condition, offering more accurate forecasts and real-time assessments to mitigate environmental threats and enhance biodiversity.

von | 06.03.24

Artificial intelligence is poised to enhance the monitoring of the ecological state of the Eckernförde Bay in the coming years
Source: Pixabay/ olleaugust

06. March 2024 ǀ Artificial intelligence is poised to revolutionize the monitoring of Eckernförde Bay’s ecological condition, offering more accurate forecasts and real-time assessments to mitigate environmental threats and enhance biodiversity.

Artificial intelligence is poised to enhance the monitoring of the ecological state of the Eckernförde Bay in the coming years. Through the integration and analysis of extensive datasets, it will enable more accurate predictions and early warnings, such as detecting potential fish die-offs. Additionally, creating a digital model of the bay could facilitate the testing of hypothetical scenarios and measures to proactively address environmental concerns. This groundbreaking initiative, a collaborative effort between GEOMAR and Kiel University, received a grant of €750,000 from Schleswig-Holstein’s Minister of Digitalization, Dirk Schrödter.

Ecological Challenges and Innovative Solutions

The bays of Schleswig-Holstein’s Baltic coast are not in good ecological condition. Seasonal oxygen-depleted zones are expanding, leading to more frequent mass fish kills. The data used to assess the state of the environment is currently often based on so-called “discrete” measurements, i.e. water samples are taken on site and then taken to the laboratory for analysis. Results can take days or even months to become available.

In the future, artificial intelligence will be used to collect, assess and develop measures to improve environmental conditions in near-real time: The pilot project INSYST (INtelligent SYSTem for Coastal Water Monitoring using Artificial Intelligence), jointly developed by GEOMAR Helmholtz Centre for Ocean Research Kiel and the University of Kiel (CAU), aims to implement this innovative monitoring concept specifically for the Eckernförde Bay. Dirk Schrödter, Schleswig-Holstein’s Minister for Digitalisation, has now awarded the project a grant of €750,000 from the state’s AI strategy.

“Environmental protection, digitalisation and artificial intelligence are inextricably linked,” says Digitalisation Minister Dirk Schrödter. “AI and data are at the centre of our efforts to make Schleswig-Holstein a digital flagship region. To achieve this goal, we are promoting AI projects in business, science and administration. We are focusing on the maritime sector. The INSYST AI project enables efficient assessment of the current water quality on our doorstep in the Bay of Eckernförde and can be used as an early warning system if the condition deteriorates. The project thus impressively demonstrates the direct benefits for biodiversity and the people of Schleswig-Holstein”.

 

Professor Dr Katja Matthes, Director of GEOMAR, emphasises the groundbreaking importance of the project: “The funding of this project is an important step towards more effective protection of the Baltic Sea. The integration of artificial intelligence into marine monitoring concepts is a promising approach, but one that has hardly been used so far. With INSYST, we can make a decisive and innovative contribution to the development of such concepts, which will help to protect and preserve our marine ecosystems in the future”.

Advancing Environmental Research with Cutting-Edge Technologies

“We are very pleased about the successful funding decision by the state. It is a special recognition of the fact that we at Kiel University can use our innovative methods and research in artificial intelligence to contribute to solving societal challenges such as improving the health of the oceans and better assessing risks in the future,” said Professor Dr Ralph Schneider, Vice-President of Kiel University.

Dr. Helmke Hepach, the project leader and environmental scientist at GEOMAR, stated that they would use the data from the Boknis Eck time series station for the project. He mentioned that since 1957, Boknis Eck had been regularly sampled monthly for various physical, chemical, and biological parameters. He further explained that sensor data from a permanently installed underwater observatory had supplemented these measurements for several years, which could be accessed live via a data cable. Dr. Hepach added that after the loss of the sensor structure in summer 2019, it would now be reinstalled at Boknis Eck. Additionally, he noted that a small-scale computer model of the Baltic Sea was available at GEOMAR, providing a good representation of the physical parameters in Eckernförde Bay.

Professor Olaf Landsiedel from the University of Kiel, who is responsible for the AI methodology in the project, mentioned that conventional statistical methods could not be applied to these large data sets. He explained that this was where artificial intelligence came in. He highlighted that AI could link and make use of these large data sets, becoming more accurate as the data sets grew. Professor Landsiedel stated that by combining the data with various AI methods, an assessment of the state of the environment could be made almost in real-time. He further explained that this allowed hypothetical scenarios and measures for environmental improvement to be tested in advance to act more effectively and sustainably. Dr. Helmke Hepach expressed hope that this would enable them to make increasingly accurate predictions and warnings.

The project could also support the monitoring efforts of the relevant authorities in Schleswig-Holstein. For example, it will help to identify suitable locations for assessing the environmental status of the Baltic Sea ecosystem or to improve the protection of marine habitats.

The data and results will also be made available to all interested parties through a communication concept. An app will allow individuals to participate in the data collection with their own observations.

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