- This course explores AI applications for sustainability, focusing on AI-driven solutions for renewable energy, pollution monitoring, and resource management. Specifically designed for environmental scientists and sustainability professionals, it equips them with AI tools to address global environmental challenges.
- Level: Intermediate
- Prerequisites: Background in environmental science or sustainability practices.
- Assessments: Micro Assessment weekly, Full Assessment in the final week.
Week 1 - AI for Climate Change Prediction and Modeling
Learning OutcomeUnderstand how AI is used to model and predict climate change and environmental impact.
1.1 AI for forecasting weather patterns and climate changes.1.2 Neural networks for analyzing large-scale environmental data.
1.3 AI in global warming and temperature rise predictions.
Practical Component
Build an AI model to predict climate change trends based on historical data.
Week 2 - AI in Renewable Energy Optimization
Learning OutcomeLearn how AI can optimize the generation and distribution of renewable energy
2.1 AI for solar and wind energy forecasting.2.2 AI-driven optimization for energy storage and grid management.
2.3 Predicting energy demand and supply using AI.
Practical Component
Create an AI solution to optimize energy generation in a renewable grid
Week 3 - AI for Pollution Monitoring and Environmental Risk Management
Learning OutcomeExplore how AI monitors pollution levels and assesses environmental risks
3.1 Using AI to analyze air quality data in real-time.3.2 AI-powered solutions for monitoring water and soil contamination.
3.3 Predicting environmental risks like wildfires or floods using AI.
Practical Component
Build an AI model to detect pollution levels from sensor data.
Week 4 - AI for Sustainable Agriculture
Learning OutcomeUnderstand how AI can optimize agriculture to ensure sustainability.
4.1 AI for crop disease prediction and pest control.4.2 Using AI for precision farming and resource allocation.
4.3 AI-driven solutions for optimizing water usage in agriculture.
Practical Component
Use AI to predict crop yield and optimize resource usage in farming.
Week 5 - AI for Waste Management and Resource Recycling
Learning OutcomeLearn how AI can enhance waste management and recycling efforts.
5.1 AI-powered waste sorting and recycling systems.5.2 Using AI for predicting and managing waste generation.
5.3 AI in optimizing resource recovery from waste materials.
Practical Component
Implement an AI-based solution to optimize waste sorting processes.
Week 6 - Capstone Project and Assessment
Learning OutcomeDevelop an AI solution for a sustainability-related problem.
6.1 Create a real-world AI solution for environmental monitoring, renewable energy optimization, or resource management.6.2 Peer assessment and feedback.
6.3 Final project presentation and evaluation.
Practical Component
Present an AI-driven solution, demonstrating its application in environmental protection or sustainability.