Seminar: Computer Science for Environmental Sustainability (CS4ES)

Data-driven approaches and computational methods are essential in addressing key environmental challenges such as climate change, biodiversity loss, and pollution control. This course explores the application of computer science concepts, techniques, and tools for advancing environmental sustainability. Students will learn how computational solutions are applied in various environmental domains, including big data analytics, machine learning, and high-performance computing.

As part of this seminar, students will create a presentation and report revolving around a research topic of their choice in English or German. Students will regularly meet with an assigned supervisor and work towards the presentation and report. The seminar will be offered in two formats: seminar and pro-seminar. The seminar will focus on scientific research, while the pro-seminar emphasizes presentation techniques. Pro-seminar students will attend two additional sessions focused on presentation skills.

The presentation time is 35 minutes (plus discussion). A short report accompanying the slides is expected (max 15 pages).

Please note that we plan to record sessions (lectures and seminar talks) with the intent of providing the recordings via BBB to other students but also to publish and link the recordings on YouTube or other interactive platforms for future terms. If you appear in any of the recordings via voice, camera or screen share, we need your consent to publish the recordings. See also this Slide.

• Understand and apply computer science concepts to solve environmental sustainability challenges.
• Compose a research-based presentation covering selected topics related to environmental informatics.
• Analyze and critique computational tools for environmental problem-solving.
• Propose innovative solutions for issues like climate change, biodiversity loss, and pollution using computational methods.

This is the list of topics we will assign during the first meeting. Students are encouraged to propose their own topics as well. Each topic is accompanied by a brief description and relevant references.

• AI and Its Environmental Impact
  • Investigate the direct and indirect environmental impacts of AI usage, including energy consumption in data centers.
  • References:
    • Wu, C. J., et al. (2022). Sustainable AI: Environmental Implications, Challenges, and Opportunities. *Proceedings of Machine Learning and Systems*, 4, 795-813.
    • https://piktochart.com/blog/carbon-footprint-of-chatgpt/

• Big Data Analytics for Environmental Monitoring
  • Explore how big data analytics helps track environmental changes through large-scale data collection and analysis.
  • References:
    • Dutta, R., et al. (2015). Big Data Architecture for Environmental Analytics. *Springer International Publishing*.
    • https://inria.hal.science/hal-01328610/document

• IoT in Environmental Monitoring
  • Discuss the use of IoT devices to monitor environmental parameters such as air quality, water pollution, and soil conditions.
  • References:
    • Shinde, S. R., et al. (2017). IoT-Based Environment Monitoring System. *International Journal of Electronics and Communication Engineering and Technology*.
    • https://caburntelecom.com/iot-environmental-monitoring/

• High-Performance Computing for Climate Modeling
  • Analyze how high-performance computing (HPC) improves climate change models, enabling more accurate predictions.
  • References:
    • André, J. C., et al. (2014). High-Performance Computing for Climate Modeling. *Bulletin of the American Meteorological Society*, 95(5), ES97-ES100.
    • https://www.nature.com/articles/s41558-023-01769-3

• Machine Learning for Predicting Climate Change
  • Investigate the application of machine learning techniques for predicting future climate trends and patterns.
  • References:
    • Fernández-Baldeón, B., et al. (2023). Machine Learning for Predicting Climate Change. *Salud, Ciencia y Tecnología-Serie de Conferencias*, 2, 465-465.
    • https://www.nature.com/articles/s41612-020-00148-5

• Geographic Information Systems (GIS) in Environmental Studies
  • Examine how GIS technologies contribute to the visualization and analysis of environmental data, such as deforestation patterns.
  • References:
    • Douglas, W. (2024). Environmental GIS Applications to Industrial Facilities. *CRC Press*.
    • https://www.ncesc.com/geographic-faq/what-is-the-role-of-gis-in-environmental-science/

• AI for Renewable Energy Optimization
  • Explore AI methods for optimizing renewable energy systems, such as solar or wind energy production and grid management.
  • References:
    • Rusilowati, U., et al. (2024). Leveraging AI for Efficiency in Energy Use and Renewable Resources. *International Transactions on Artificial Intelligence*, 2(2), 114-120.
    • https://journals.sagepub.com/doi/10.1177/0958305X241256293

• Blockchain for Environmental Sustainability
  • Discuss how blockchain technology ensures transparency and traceability in environmental initiatives like carbon trading.
  • References:
    • Jan, A., et al. (2024). Blockchain for Environmental Sustainability. *Business Strategy and the Environment*, 33(3), 2004-2019.
    • https://www.mdpi.com/2071-1050/12/10/4037

• Ethical Considerations of AI in Environmental Sustainability
  • Explore the ethical issues surrounding AI deployment in sustainability projects, focusing on bias and environmental justice.
  • References:
    • Farooq, S., et al. (2024). Balancing Environmental Sustainability and Privacy in AI-Enabled Smart Cities. *IGI Global*.
    • https://link.springer.com/chapter/10.1007/978-3-031-48135-2_11

• Smart Cities and Sustainable Urban Development
  • Analyze how computer science is used to build smart cities that promote sustainable urban growth and resource management.
  • References:
    • Sharifi, A., et al. (2024). Smart Cities and Sustainable Development Goals (SDGs). *Cities*, 146, 104659.
    • https://www.mdpi.com/2071-1050/10/8/2818

• AI for Biodiversity Conservation
  • Investigate the use of AI tools to monitor and protect biodiversity through habitat analysis and species identification.
  • References:
    • Vasiliev, D., et al. (2024). AI Applications in Biodiversity Conservation. *Springer Nature Singapore*.
    • https://www.nature.com/articles/s41893-022-00851-6

• Pollution Monitoring with Computer Science Tools
  • Explore the role of data science and AI in monitoring and reducing air and water pollution levels.
  • References:
    • Silva, D., et al. (2024). Monitoring Pollution Levels in Smart Cities. *Springer Nature Switzerland*.
    • https://www.nature.com/articles/s41598-024-71269-7

The exam is conducted as part of the presentation (50% of the mark) and report (50%). The focus for pro-seminars lies in effective presentation skills, while the focus for seminars is the depth of the scientific topic.

• 05.11.24 - Introduction to Course & Environmental Challenges
  • Course overview, introduction to environmental challenges, formation of research groups.

• 12.11.24 - Lecture 1: Data Science Applications in Environmental Studies
  • Topics: IoT, Machine Learning (ML), Big Data in environmental monitoring.

• 19.11.24 - Lecture 2: Computer Science Methods for Climate Change, Biodiversity, and Pollution Control
  • High-Performance Computing (HPC) for climate modeling, GIS for biodiversity conservation, and pollution control.

• 26.11.24 - Research Topic Discussion & Selection
  • Topic discussion and finalization. Group research begins with regular check-ins.

• 03.12.24 - Lecture 3: Applications in Sustainable Agriculture, Renewable Energy, and Waste Management
  • AI-driven precision agriculture, renewable energy system optimization, and waste management with computer science.

• 10.12.24 - Group Work & Check-ins
  • Begin group research with regular supervisor check-ins and guidance.

• 17.12.24 - Group Presentations (Pre-Midterms)
  • First round of group presentations on selected research topics.

• 24.12.24 - Midterm Break (No Session)

• 07.01.25 - Group Presentations (Pre-Midterms)
  • Continue group presentations on selected research topics.

• 14.01.25 - Lecture 4: Ethical Considerations in Environmental Sustainability
  • Ethical issues in AI and computer science solutions for environmental protection.

• 21.01.25 - Advanced Research & Report Writing
  • Research finalization and guidance on writing the report. Assistance with structuring and improving the research output.

• 28.01.25 - Final Presentations
  • Group presentations of the final research results.

• 04.02.25 - Backup Presentation Session
  • A session for backup presentations in case of any scheduling issues.

• 31.03.25 - Deadline for Submission of the Final Report
  • Submission of final reports (max. 15 pages).

Relevant reading materials will be shared throughout the course. Students are encouraged to contact the instructors for early preparation and recommended readings.