AI in Environment & Climate
Build. Automate. Improve.
The course aims to empower learners to:
1. Understand global environmental challenges like climate change, pollution, and biodiversity loss.
2. Track and monitor environmental data using AI tools, IoT devices, and satellite imaging.
3. Predict weather patterns and climate risks through machine learning models and big data.
4. Conserve natural resources by creating AI-powered energy, water, and waste management solutions.
5. Gain practical experience with real datasets, sensors, and visualization tools to create climate action plans.
Target Audience: Environmental researchers, urban planners, government officials, students, NGO workers, sustainability consultants, and anyone passionate about conservation.
Module 1:
The Science of Climate & Environment
Topics:
- Introduction to ecosystems (air, water, land, and biodiversity).
- The science of global warming and greenhouse gases.
- Human activities vs. natural climate variability.
- International frameworks: Paris Agreement, IPCC, SDGs (Goal 13).
Hands-On Activity:
Create a climate change timeline using AI tools like ChatGPT for data summarization.
Module 2:
Environmental Data Tracking
Topics:
- IoT sensors for environmental monitoring (air quality, water testing, soil health).
- Open-source datasets: OpenAQ, NOAA, NASA EarthData.
- Mobile apps & smart meters for personal carbon footprint tracking.
Tools: BreezoMeter, AirVisual, ThingSpeak IoT platform.
Hands-On: - Build a basic air quality index dashboard using open data + Power BI.
Module 3:
Weather Forecasting & Predictive Analysis
Topics:
- Basics of weather forecasting and machine learning models.
- AI models for predicting rainfall, hurricanes, and droughts.
- Satellite imagery: Google Earth Engine, MeteoBlue.
Hands-On: - Analyze 5-year rainfall patterns of a region using Google Earth Engine.
Module 4:
Resource Conservation (Water, Energy, Waste)
Topics:
- AI for energy conservation: smart grids, consumption prediction.
- Water management with AI: smart irrigation, leak detection.
- Waste reduction with AI-powered sorting & recycling.
Tools: EcoBee, Sense, Siemens MindSphere.
Hands-On: - Develop a home/office energy-saving plan with AI-suggested solutions.
Module 5:
Disaster Risk & Climate Resilience
Topics:
- AI for early disaster warnings (wildfires, floods, cyclones).
- AI-based evacuation and recovery planning.
- Predictive disaster modeling (historical vs. real-time data).
Tools: ClimateAI, NASA FIRMS (Fire Information for Resource Management System).
Hands-On:
Map flood-prone areas using satellite data.
Module 6:
Biodiversity Monitoring & Deforestation Tracking
Topics:
- Use of drones, satellite images, and AI in wildlife monitoring.
- Tools for deforestation analysis: Global Forest Watch, WWF Data Lab.
- Understanding species extinction patterns through AI.
Hands-On:
Detect deforestation changes in the Amazon with satellite imagery.
Module 7:
Smart Cities & Sustainable Urban Development
Topics:
- AI for urban climate monitoring (heat mapping, water pollution).
- Smart traffic and renewable energy systems for cities.
- Green infrastructure: vertical gardens, rainwater harvesting, AI irrigation.
Hands-On: - Design a mini Smart Green City model with IoT dashboards.
Module 8:
Capstone Project – Track. Predict. Conserve.
Project Examples:
- Build a climate monitoring dashboard for a city.
- Design an AI-powered water conservation plan for a community.
- Create a biodiversity tracking solution using open data.
Final Deliverable:
A complete climate action portfolio with dashboards, reports, and AI-powered recommendations.
Learning Tools & Platforms Used
- Data Platforms: Google Earth Engine, NOAA, NASA EarthData.
- Visualization Tools: Power BI, Tableau, ArcGIS.
- IoT Devices: BreezoMeter, EcoBee, AirVisual sensors.
- Prediction Tools: IBM Weather AI, ClimateAI.
- Geospatial Tools: Global Forest Watch, Google Maps API.
- AI Assistants: ChatGPT for analysis & data summarization.
📈 Learning Outcomes
By the end of this course, learners will:
After completing the course, learners will:
- Understand environmental and climate science fundamentals.
- Track and monitor real-time environmental data with IoT and open datasets.
- Build AI-powered predictive models for weather and disaster risks.
- Propose energy and water-saving strategies using AI-driven insights.
- Develop biodiversity and deforestation monitoring solutions using satellite imagery.
- Create visual reports and dashboards for environmental trends.
- Design climate resilience projects for smart cities and urban spaces.
- Present a capstone project portfolio showcasing real-world solutions.
Duration:
| Section | Details |
| Total Duration | 12 Weeks (4 hours per week) |
| Weeks 1–9 | Detailed learning modules with hands-on labs. |
| Week 10 | Industry-focused case studies. |
| Week 11 | Capstone Project: Real-world problem-solving. |
| Week 12 | Final review, portfolio creation, and presentations. |