Predictive Analytics for Climate-Sensitive Sectors

About This Course

Climate change is no longer a distant risk—it’s already shaping crop yields, water availability, energy demand, and public health outcomes. The challenge for professionals today isn’t only understanding climate trends, but predicting what’s likely to happen next and planning early.

This intensive international course is built to be practical and hands-on. You’ll work with real datasets from trusted open sources, learn how to clean and structure climate data properly, and then build predictive models that can support planning and risk reduction. Along the way, you’ll also learn how to visualize environmental risks on maps and communicate results through simple dashboards—so your predictions don’t stay stuck in notebooks, but actually become useful for teams and decision-makers.

Aim

To equip participants with the tools, techniques, and predictive models needed to forecast climate-related risks and opportunities in critical sectors—supporting better planning, sustainable resource management, and resilient policy decisions.

Course Objectives

By the end of this course, participants will be able to:

• Use AI + statistical methods to understand and manage climate impacts

• Turn raw climate data into actionable forecasting insights

• Build predictive models for variables like rainfall, temperature, and sector outcomes (e.g., crop yield)

• Apply geospatial analysis to identify risk zones (drought, flood-prone regions, heat hotspots)

• Evaluate model quality using the right metrics and error checks

• Communicate outputs clearly through maps and interactive dashboards

• Collaborate across climate science, sector experts, and data/AI teams

Course Structure

Module 1: Climate Data and Predictive Analytics Foundations

• A clear overview of climate-sensitive sectors and why they’re vulnerable
  (agriculture, water, energy, public health)

• Predictive analytics basics you’ll actually use:

  • regression, classification, and time-series forecasting concepts

• Finding and using open climate datasets:

  • NASA EarthData, NOAA, Copernicus, IPCC

• Data preparation that makes or breaks your model:

  • handling missing values

  • time indexing and seasonal formatting

  • spatial tagging and location consistency

• Visualizing climate trends in a readable way (not messy charts)

🛠️ Hands-on tools: Python (Pandas, NumPy, Plotly), Jupyter Notebook

Module 2: Predictive Modeling and Geospatial Analysis

• ML methods commonly used for climate datasets:

  • ARIMA, Random Forest, XGBoost

• Predicting climate variables and sector outcomes:

  • rainfall, temperature, and examples like crop yield forecasting

• Geospatial fundamentals (made simple):

  • raster vs vector

  • spatial layers and overlays

  • mapping climate patterns on real regions

• Risk area detection:

  • drought zones, flood risk regions, climate zoning approaches

• Building models end-to-end:

  • training, testing, validation—and avoiding overconfidence

🛠️ Hands-on tools: Scikit-learn, GeoPandas, Folium, QGIS, Google Earth Engine

Module 3: Applications, Dashboards, and Capstone Projects

• Real-world case studies across sectors:

  • agriculture forecasting

  • hydrology and water risk

  • heat exposure and health risk models

• Building a complete predictive pipeline:

  • data ingestion → modeling → evaluation → visualization

• Model evaluation that decision-makers can trust:

  • MAE, RMSE, R², bias checks and interpretation

• Turning results into something usable:

  • interactive climate dashboards with Streamlit

• Capstone-style mini project:

  • each group/team builds a small prediction + visualization workflow they can reuse

🛠️ Hands-on tools: Streamlit, GitHub, Python (Matplotlib, Altair)

Who Should Enrol?

• Climate scientists and environmental engineers

• Professionals in agriculture, water, energy, and public health sectors

• Data analysts and AI/ML practitioners working in sustainability

• Government planners, policy teams, and development agencies

• Postgraduate students and researchers in climate and data science