Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This 3‑day hands‑on course trains participants to build predictive AI systems for disaster management—from flood and heat‑wave forecasting to satellite‑based damage detection and deployment‑ready climate‑risk tools. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day hands‑on course trains participants to build predictive AI systems for disaster management—from flood and heat‑wave forecasting to satellite‑based damage detection and deployment‑ready climate‑risk tools.
📋 Course Objectives
1. Apply AI in Sustainability & Climate methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI in Sustainability & Climate
• R&D engineers and working professionals applying AI in Sustainability & Climate in industry
• Academics and educators building research or teaching capacity in AI in Sustainability & Climate
🚀 Key Learning Outcomes
• A demonstrable AI in Sustainability & Climate project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Forecasting the Extreme (Predictive Analytics)
Master physics‑informed ML to embed fluid‑dynamic constraints in deep models • Implement Transformer & LSTM time‑series models for sub‑seasonal flood & heat‑wave forecasts • Deploy a Colab project – build a flood predictor with NASA GloFAS data
Day 2 – Real‑time Intelligence & Spatial Risk
Fuse SAR and optical Sentinel imagery to see through clouds during storms • Create automated change‑detection pipelines with Vision Transformers and Siamese networks • Integrate AI‑derived risk maps into ArcGIS/QGIS digital twins for urban adaptation
Day 3 – Deployment, Ethics & Resilient Infrastructure
Quantize models for edge AI on drones and IoT sensors for offline wildfire detection • Apply SHAP & LIME to generate transparent explanations for evacuation decisions • Address algorithmic fairness to protect vulnerable populations in data‑desert regions
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow Lite |
| Covered Tool / Platform | NASA GloFAS |
| Covered Tool / Platform | Sentinel-1 SAR |
| Covered Tool / Platform | Sentinel-2 optical |
| Covered Tool / Platform | ArcGIS |
| Covered Tool / Platform | QGIS |
| Covered Tool / Platform | SHAP |
Frequently Asked Questions
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