AI in Environmental Engineering
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Follow on Instagram for UpdatesAI in Environmental Engineering Syllabus — Unit-wise Content
The official GTU curriculum for AI in Environmental Engineering (subject code DI05013071) as prescribed for Mining · Semester 5 divides the course into 5 units, covering 17 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI05013071)Unit 1 Introduction to
- Artificial Intelligence and Machine learning in Environmental Engineering Definition and scope of Artificial Intelligence Difference between AI, Machine Learning, and Data Science Types of environmental data: Time-series (air/noise monitoring data)
- Spatial data (GIS-based environmental data)
- Sensor-based IoT data Machine Learning types: Supervised learning
- Unsupervised learning
- Basic workflow of ML model development Types of AI: Rule-based systems
- learning-based systems Role of AI in environmental engineering Overview of AI applications: Air quality prediction
- Water quality monitoring
- Noise level prediction
- Solid waste management
- Climate and environmental risk assessment Case examples from smart cities and environmental monitoring projects
Unit 2 AI Techniques
- for Environmental Modelling Regression techniques for prediction (linear regression concept) Classification techniques (basic idea of decision trees
- k-NN) Clustering for environmental data grouping (k-means concept) Introduction to Artificial Neural Networks (conceptual) Model training
- validation, and testing (basic concepts) Performance indicators: Accuracy Mean Square Error (conceptual understanding) Limitations and uncertainties in AI models for environmental systems
Unit 3 Applications of
- AI in Environmental Engineering AI in Air Pollution Control Prediction of AQI Source apportionment (concept) AI in Water and Wastewater Engineering Water quality prediction Process optimization in treatment plants AI in Noise Pollution Studies Noise level prediction models Smart noise monitoring AI in Solid Waste Management Route optimization for waste collection Waste segregation using AI (concept) Program Name: Diploma Engineering Level: Diploma Branch: Environmental Engineering Subject Code : DI05013071 Subject Name: AI in Environmental Engineering AI for Environmental Impact Assessment (EIA) and risk assessment Case studies from Indian and global context
Unit 4 Tools,
- Software and Practical Implementation Introduction to user-friendly AI tools: Spreadsheet-based analysis Online ML platforms (conceptual exposure) Basics of environmental datasets handling Simple demonstration of: Training a basic prediction model (conceptual/practical exposure) Introduction to GIS integration with AI (conceptual) Interpretation of results for decision-making Reporting AI outputs in technical reports
Unit 5 Ethics,
- Sustainability and Future Trends Ethical issues in AI: bias
- data privacy Reliability and responsibility in environmental decision support systems Sustainability aspects of AI-based environmental solutions Future trends: Smart cities Digital twins AI-driven environmental governance Career opportunities in AI for environmental engineers
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