AI in Environment - 2026 entry
| MODULE TITLE | AI in Environment | CREDIT VALUE | 15 |
|---|---|---|---|
| MODULE CODE | COMM119 | MODULE CONVENER | Unknown |
| DURATION: TERM | 1 | 2 | 3 |
|---|---|---|---|
| DURATION: WEEKS | 11 |
| Number of Students Taking Module (anticipated) | 40 |
|---|
DESCRIPTION - summary of the module content
AI is playing a crucial role in addressing environmental challenges by enabling data-driven decision-making and sustainable solutions. In this module, you will study the fundamental concepts of AI and its applications in environmental problems, including for example, geographical data analysis, machine learning, and foundation models for climate. You will study the applications of AI in environment, such as climate change mitigation and biodiversity conservation. You will attend lectures complemented by lab sessions or discussion, where you will apply AI techniques to real-world environmental datasets and analyse case studies. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and fundamental machine learning concepts.
Module Pre-Requisites: COMM113 Deep Learning
AIMS - intentions of the module
This module aims to equip you with the knowledge and skills to apply AI technologies in addressing environmental challenges. You will explore key concepts, for example, machine learning, computer vision, and time-series data analysis, and examine their applications such as environmental monitoring and climate modelling. You will learn to analyse real-world challenges, such as weather forecasting, disaster management, and pollution monitoring, and formulate them as machine learning problems. Additionally, you will assess the performance and impact of AI solutions across various environmental applications, considering both technological and ethical implications. Through hands-on projects and case studies, you will gain practical experience in developing AI-driven solutions for environmental challenges, preparing you for careers in environment-related research, policy-making, or industry applications.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
Module Specific Skills and Knowledge:
1 Explain the fundamental concepts of AI and its applications in environmental problems, including for example, machine learning and computer vision in areas such as climate change, biodiversity conservation, and pollution control.
2 Formulate real-world environmental challenges as machine learning problems.
3 Design AI-based solutions to address environmental challenges, such as weather forecasting, disaster management, and ecosystem monitoring.
4 Evaluate the designed AI solutions for limitations, strengths, improvements required, etc.
Discipline Specific Skills and Knowledge:
5 Critically analyse and evaluate the performance and impact of AI in various applications.
6 Identify the compromises and trade-offs which must be made when translating AI theory into practice.
Personal and Key Transferable/ Employment Skills and Knowledge:
7 Effectively communicate insights and evaluations drawn from research papers.
SYLLABUS PLAN - summary of the structure and academic content of the module
Concepts and Theoretical Foundations
Introduction to AI for environmental challenges.
Introduction to deep learning and advanced model structures.
Data sources and challenges in environmental AI.
Implementation and Practical Techniques
For example, time-series analysis for climate prediction and computer vision for environmental applications.
Analysis and Evaluation
Quantitative and qualitative evaluation techniques in AI for environment.
Ablation study.
Real-world case studies and advanced applications.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
| Scheduled Learning & Teaching Activities | 33 | Guided Independent Study | 117 | Placement / Study Abroad | 0 |
|---|
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
| Category | Hours of study time | Description |
| Scheduled Learning & Teaching activities | 22 | Lectures |
| Scheduled Learning & Teaching activities | 11 | Workshops/tutorials |
| Guided independent study | 45 | Coursework preparation and completion |
| Guided independent study | 72 | Wider reading and self-study |
ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
| Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
|---|---|---|---|
| Practical Exercises | 10 | All | Answers to exercises and oral feedback |
SUMMATIVE ASSESSMENT (% of credit)
| Coursework | 30 | Written Exams | 70 | Practical Exams |
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DETAILS OF SUMMATIVE ASSESSMENT
| Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
|---|---|---|---|---|
| Continuous assessment | 30 | 30 hours | All | Written |
| Written exam – closed book | 70 | 2 hours | 1,3,4,5,6 | Written |
DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
| Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
|---|---|---|---|
| Continuous assessment | Continuous assessment | All | Ref/Def period |
| Written exam – closed book | Written exam – closed book | 1,3,4,5,6 | Ref/Def period |
RE-ASSESSMENT NOTES
Reassessment will be by coursework/quiz in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
• Kumari, N. and Pandey, S., 2023. Application of artificial intelligence in environmental sustainability and climate change. In Visualization techniques for climate change with machine learning and artificial intelligence (pp. 293-316). Elsevier.
• Nishant, R., Kennedy, M. and Corbett, J., 2020. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International journal of information management, 53, p.102104.
• Srivastava, A.N., Nemani, R. and Steinhaeuser, K. eds., 2017. Large-scale machine learning in the earth sciences. CRC Press.
• Hassanien, A.E., Darwish, A. and Elghamrawy, S.M. eds., 2024. Artificial Intelligence for Environmental Sustainability and Green Initiatives. Springer Nature Switzerland, Imprint: Springer.
• Silvestro, D., Goria, S., Sterner, T. and Antonelli, A., 2022. Improving biodiversity protection through artificial intelligence. Nature sustainability, 5(5), pp.415-424.
• Fan, Z., Yan, Z. and Wen, S., 2023. Deep learning and artificial intelligence in sustainability: a review of SDGs, renewable energy, and environmental health. Sustainability, 15(18), p.13493.
Web-based and electronic resources:
• ELE – Faculty to provide hyperlink to appropriate pages
Reading list for this module:
| CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
|---|---|---|---|
| PRE-REQUISITE MODULES | None |
|---|---|
| CO-REQUISITE MODULES | None |
| NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
|---|---|---|---|
| ORIGIN DATE | Monday 11th November 2024 | LAST REVISION DATE | Wednesday 6th August 2025 |
| KEY WORDS SEARCH | Environmental intelligence, AI, machine learning |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.