Predictive Analytics for Climate-Sensitive Sectors
Leveraging Data-Driven Forecasting for Resilience, Risk, and Resource Optimization
Skills you will gain:
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.
- 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.
What you will learn?
📅 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
Intended For :
Career Supporting Skills