Urban Metabolism Modeling with AI

About This Course

This 3-week course helps you understand how cities “eat, use, and recycle” resources—energy, water, and waste—and how AI can make those flows visible, measurable, and optimizable. You’ll learn how to find and clean real urban datasets (IoT, satellite, open city dashboards), build AI models for forecasting and anomaly detection, and turn your results into interactive dashboards that support planning and policy decisions.
By the end, you’ll have a practical workflow you can reuse for smart city analytics, urban sustainability projects, and urban digital twins.

Aim

To enable participants to apply AI-driven modeling and simulation for tracking and improving urban resource flows (energy, water, waste) in megacities—supporting smarter decision-making, sustainable planning, and policy-ready insights.

Course Structure

Module 1 — Foundations & Data Understanding

• Urban metabolism made simple: what it is and why it matters for megacities

• Global snapshots: how different cities manage energy, water, and waste (and where they struggle)

• Resource flow mapping: turning messy city systems into understandable inputs and outputs

• Finding urban data: IoT sensors, satellite sources, city open dashboards, utility-style datasets

• Hands-on: Collect, clean, and preprocess real urban datasets for modeling

Module 2 — AI for Resource Flow Modeling

• Where AI fits in urban systems: forecasting, optimization, early warning, and operations support

• Machine learning for demand and flow prediction (energy/water trends, seasonality, spikes)

• Anomaly detection in utilities: spotting leaks, abnormal consumption, overflow risks, and sensor issues

• Hands-on: Build AI models to forecast energy and water demand using real-world style data

Module 3 — Simulation, Visualization & Real-World Application

• From models to action: simulation thinking and how digital twins support smart cities

• Visualizing flows in a way people can use: QGIS-style maps + Streamlit dashboards

• Communicating results for policy and planning: turning metrics into clear, decision-ready stories

• Hands-on: Build a city resource dashboard that combines forecasts + anomaly alerts + practical insights

Who Should Enrol?

• Students, researchers, and professionals in urban planning, AI/ML, sustainability, environmental studies

• Policymakers, city planners, and engineers working on smart cities and resource management

• Anyone who wants to apply AI to build future-ready, sustainable urban systems