AI and Automation in Environmental Hazard Detection

About This Course

“AI and Automation in Environmental Hazard Detection” is a dynamic 3-week course that delves into how Artificial Intelligence (AI), Machine Learning (ML), and automation are transforming the way we monitor and respond to environmental risks. By integrating remote sensing, IoT sensor networks, satellite imagery, and predictive analytics, this course enhances public safety and ecological resilience. Participants will gain hands-on experience using powerful tools like Google Earth Engine, YOLO/Deep Learning for image detection, scikit-learn for environmental datasets, and Arduino/Raspberry Pi for automated alert systems.

This course prepares participants to harness AI for real-time detection, prediction, and mitigation of environmental hazards, ranging from floods and wildfires to chemical leaks and air pollution.

Aim

The course aims to train participants in leveraging AI, ML, and automation technologies for environmental hazard detection. It empowers individuals to use advanced tools for timely intervention, predictive monitoring, and improving disaster preparedness and response.

Course Objectives

• Introduce advanced AI and automation tools for environmental applications

• Bridge the gaps between climate data, sensors, and AI models

• Foster interdisciplinary collaboration between technology and environmental sectors

• Enable participants to contribute to climate adaptation and disaster preparedness

• Provide open-access tools and datasets for continued innovation

Course Structure

📅 Module 1: Introduction to AI in Environmental Hazard Detection

Theme: Understanding AI’s Role in Environmental Risk Monitoring

• Environmental Hazards & AI’s Role

  • Overview of hazards: pollution, natural disasters, and other environmental threats

  • AI’s transformative capabilities in detection & prediction

• AI & Machine Learning Fundamentals

  • Key ML techniques for hazard analysis

  • Data preprocessing & algorithm selection

Hands-On Lab:

• Setting up AI environments

• Building basic predictive models for hazard detection

📅 Module 2: Advanced AI Techniques for Hazard Detection

Theme: Deep Learning and Remote Sensing for Advanced Detection

• Remote Sensing & AI for Monitoring

  • Leveraging satellite/drone data and GIS integration

  • AI-powered hazard tracking and detection

• Deep Learning for Hazard Detection

  • Optimizing CNNs & RNNs for environmental threats

  • Model training & performance tuning

Hands-On Lab:

• Processing real-world satellite imagery

• Training deep learning models for environmental hazard prediction

📅 Module 3: Automation & Real-Time Monitoring

Theme: Building Real-Time Systems for Hazard Detection and Response

• AI & IoT for Real-Time Monitoring

  • Combining sensor networks with AI for instant hazard alerts

  • Utilizing edge computing for rapid response to environmental threats

• Automating Hazard Detection & Response

  • AI-driven decision pipelines for disaster management

  • Case studies in wildfire/flood prediction and other real-time hazard detection

Hands-On Lab:

• Developing an automated detection prototype

• Simulating emergency alert scenarios with IoT and AI systems

Who Should Enrol?

• Environmental scientists and disaster management professionals

• Data scientists and engineers working in sustainability

• Urban planners, civil engineers, and remote sensing experts

• Government agency representatives and NGO workers

• UG/PG/PhD students in environmental science, AI, or geoinformatics