Programme Parameters
Graduate / Intermediate
4 Weeks (40 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
Urban Metabolism Modeling with AI treats the city as a living system that consumes resources and produces waste, and shows how data and machine learning help understand and improve it. You learn the urban-metabolism framework — tracking flows of energy, water, materials and waste — and how AI models these flows from sensor, utility and geospatial data. The course connects the analysis to decisions: identifying inefficiencies, forecasting demand, and planning more circular, resource-efficient cities. Set within sustainability and smart-city goals, it turns a complex systems view into actionable insight. You finish able to reason about modelling a city’s metabolism with AI. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to urban metabolism — modelling the flows of energy, water, materials and waste through cities to support sustainable urban planning.
📋 Course Objectives
1. Explain the urban-metabolism framework.
2. Model flows of energy, water, materials and waste.
3. Use AI on sensor, utility and geospatial data.
4. Identify inefficiencies and forecast demand.
5. Connect analysis to circular-city planning.
👥 Who Should Enroll?
• Urban planners and sustainability analysts
• Smart-city and environmental professionals
• Data scientists in urban systems
• Students of urban sustainability
🚀 Key Learning Outcomes
• An understanding of AI-driven urban metabolism.
• A resource-flow modelling perspective.
• A systems view of sustainable cities.
• 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
AI Fundamentals, Mathematics, and Urban Metabolism Modeling Foundations
Develop a comprehensive understanding of AI and machine learning concepts, including supervised and unsupervised learning techniques, to apply in urban metabolism modeling contexts • Analyze mathematical foundations of AI, including linear algebra, calculus, and probability, to inform urban metabolism modeling decisions • Design and implement basic AI models using Python and relevant libraries to solve urban metabolism problems
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets for urban metabolism modeling, including data cleaning, preprocessing, and feature engineering • Evaluate and select appropriate data preprocessing techniques, such as handling missing values and data normalization, to improve model performance • Implement data pipelines using tools like Apache Beam or AWS Glue to streamline data processing for urban metabolism modeling
Model Architecture, Algorithm Design, and Urban Metabolism Modeling Methods
Design and implement deep learning models, including convolutional neural networks and recurrent neural networks, for urban metabolism modeling tasks • Develop and evaluate algorithmic approaches, such as reinforcement learning and transfer learning, to solve complex urban metabolism problems • Optimize model architecture and hyperparameters using techniques like grid search and Bayesian optimization to improve urban metabolism modeling performance
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate AI models using various metrics, including accuracy, precision, and recall, to assess urban metabolism modeling performance • Implement hyperparameter optimization techniques, such as random search and gradient-based optimization, to improve model performance • Analyze and interpret model results, including visualizing predictions and evaluating uncertainty, to inform urban metabolism decisions
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments, including cloud-based and edge-based deployments, to support urban metabolism applications • Develop and implement MLOps pipelines, including model monitoring and updating, to ensure continuous urban metabolism modeling performance • Configure and manage production workflows, including data ingestion and model serving, to support scalable urban metabolism modeling
Ethics, Bias Mitigation, and Responsible AI Practices
Evaluate and mitigate bias in AI models, including data bias and algorithmic bias, to ensure fair urban metabolism modeling outcomes • Develop and implement responsible AI practices, including transparency and explainability, to support trustworthy urban metabolism modeling • Analyze and address ethical considerations, including privacy and accountability, in urban metabolism modeling contexts
Industry Integration, Business Applications, and Case Studies
Develop and evaluate business cases for urban metabolism modeling, including cost-benefit analysis and ROI calculation • Implement and deploy AI models in industry contexts, including integration with existing systems and infrastructure • Analyze and present case studies of successful urban metabolism modeling applications, including lessons learned and best practices
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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