Predictive Analytics for Climate-Sensitive Sectors
Leveraging Data-Driven Forecasting for Resilience, Risk, and Resource Optimization
About This Course
This intensive international course is designed to develop practical skills in predictive analytics applied to climate-sensitive sectors including agriculture, water, energy, and public health. Through interactive sessions using real-world datasets and freely available tools, participants will learn to build predictive models, visualize environmental risks, and support climate-resilient decision-making.
Aim
To equip participants with the analytical tools, techniques, and models required to forecast climate-related risks and opportunities in critical sectors, enabling informed planning, sustainable resource management, and policy resilience.
Workshop Objectives
- Develop practical skills in climate data engineering and predictive analytics.
- Apply machine learning to forecast risks across climate-sensitive sectors.
- Use explainable AI and scenario analysis for climate-risk decision-making.
- Build hands-on climate prediction workflows using Google Colab.
Workshop Structure
📅 Day 1: Climate Data Engineering & Sector Risk Mapping
Core Objective: Build climate-sensitive datasets and identify key environmental drivers affecting agriculture, water, energy, and infrastructure.
- Climate data preprocessing, missing-value handling, anomaly detection, and time-series structuring
- Feature engineering using temperature, precipitation, humidity, soil moisture, and extreme-event indicators
- Mapping climate variables with sector-specific risks such as drought, crop stress, water scarcity, and heat exposure
🛠️ Hands-on Lab: Build a Python-based climate-risk dataset, generate lag and rolling features, and create sector-specific climate risk indicators using Google Colab.
🧰 Tools Covered: Pandas, NumPy, Scikit-learn, Matplotlib, Google Colab
📅 Day 2: Machine Learning for Climate Risk Forecasting
Core Objective: Develop predictive models to forecast climate-sensitive sector risks using machine learning and time-series analytics.
- Machine learning for drought, crop-risk, water-demand, renewable-energy, and heat-risk forecasting
- Time-series feature engineering, temporal train-test splitting, and climate forecasting workflows
- Comparing Random Forest, Gradient Boosting, and XGBoost models using MAE, RMSE, and R²
🛠️ Hands-on Lab: Train a machine learning model to forecast climate-related sector risk and compare predicted versus observed risk trends using Google Colab.
🧰 Tools Covered: Scikit-learn, XGBoost, Pandas, Matplotlib, Google Colab
📅 Day 3: Explainable AI & Climate Scenario Stress Testing
Core Objective: Interpret predictive climate models and evaluate sector vulnerability under changing and extreme climate scenarios.
- Explainable AI using feature importance and SHAP to identify major climate-risk drivers
- Climate scenario analysis for temperature rise, rainfall deficit, extreme heat, and compound climate events
- Translating predictive outputs into climate-risk scores, early-warning indicators, and decision-support insights
🛠️ Hands-on Lab: Apply SHAP to a climate-risk prediction model and simulate temperature and rainfall stress scenarios to evaluate changes in sector vulnerability using Google Colab.
🧰 Tools Covered: SHAP, XGBoost, Scikit-learn, Pandas, Google Colab
Who Should Enrol?
- Researchers & Ph.D. Scholars
- Academicians & Faculty Members
- Climate & Environmental Scientists
- Data Scientists & Analysts
- Sustainability & ESG Professionals
- Energy, Agriculture & Water-Sector Professionals
- Environmental Engineers & Consultants
- Professionals interested in AI-driven climate risk analytics
Important Dates
Registration Ends
September 7, 2026
IST 04: 00 PM
Workshop Dates
September 7, 2026 – September 9, 2026
IST 05:30 PM IST
Workshop Outcomes
- Prepare and analyze climate-sensitive datasets.
- Engineer climate-risk features for predictive modeling.
- Build and evaluate machine learning forecasting models.
- Apply SHAP for explainable climate-risk predictions.
- Perform climate scenario and stress-testing analysis.
- Translate model outputs into actionable climate-risk insights.
Recording fee
Participants in India
₹2,499
Participants outside India
$50
- All session recordings
- Study material
- Certificate after completion
- 1-year access
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy