Artificial Neural Networks In Vehicular Pollution Modelling Studies in Computational Intelligence 41 1st edition by Mukesh Khare, Shiva Nagendra ISBN 3540374175  978-3540374176

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ISBN 10: ‎ 3540374175
ISBN 13: 978-3540374176
Author: Mukesh Khare, Shiva Nagendra 

Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing step-by-step procedure for formulation and development of ANN based VP models considering meteorological and traffic parameters. The model predictions are compared with existing line source deterministic/statistical based models to establish the efficacy of the ANN technique in explaining frequent dispersion complexities in urban areas.

The book is very useful for hardcore professionals and researchers working in problems associated with urban air pollution management and control.

Artificial Neural Networks In Vehicular Pollution Modelling (Studies in Computational Intelligence, 41) 1st Table of contents:

1. ntroduction to Vehicular Pollution and Environmental Impact
   1.1 Overview of Air Pollution and its Sources
   1.2 The Role of Vehicular Emissions in Environmental Degradation
   1.3 Importance of Modelling Pollution for Policy and Regulation

2. Introduction to Artificial Neural Networks (ANNs)
   2.1 Fundamentals of Neural Networks
   2.2 Types of Neural Networks and their Applications
   2.3 The Role of ANNs in Environmental Modelling

3. Pollution Modelling: Methods and Approaches
   3.1 Conventional Approaches to Pollution Modelling
   3.2 The Need for AI-based Approaches in Pollution Modelling
   3.3 Challenges in Modelling Vehicular Pollution

4. Artificial Neural Networks for Pollution Prediction
   4.1 Basics of Neural Network Architecture for Pollution Models
   4.2 Data Input Features for Pollution Models (Traffic, Weather, etc.)
   4.3 Training and Evaluation of ANN Models in Pollution Prediction

5. Data Collection and Preprocessing for ANN Models
   5.1 Types of Data Required for Pollution Modelling
   5.2 Data Sources: Traffic Data, Air Quality Monitoring Stations, etc.
   5.3 Data Preprocessing Techniques for Neural Network Training

6. Case Studies: ANN Applications in Vehicular Pollution Modelling
   6.1 Modelling NOx and CO2 Emissions in Urban Areas
   6.2 Traffic Flow and its Impact on Emission Levels
   6.3 Real-Time Pollution Forecasting using Neural Networks

7. Evaluating the Performance of ANN Models
   7.1 Key Evaluation Metrics for Pollution Models
   7.2 Cross-Validation and Model Selection Techniques
   7.3 Comparison with Traditional Pollution Modelling Methods

8. Advanced Neural Network Techniques for Pollution Modelling
   8.1 Deep Learning Approaches in Pollution Modelling
   8.2 Hybrid Models: Combining ANNs with Other AI Techniques
   8.3 Transfer Learning in Environmental Modelling

9. Applications of ANN-based Pollution Models in Policy and Decision-Making
   9.1 Influence of Pollution Models on Urban Planning and Traffic Management
   9.2 Regulatory and Environmental Impact Assessments
   9.3 Public Awareness and Policy Development Using AI Models

10. Challenges and Future Directions in Vehicular Pollution Modelling
    10.1 Limitations of Current ANN Models in Pollution Prediction
    10.2 The Role of Big Data and IoT in Enhancing Pollution Models
    10.3 Future Trends in AI for Environmental Modelling

11. Conclusion
    11.1 Summary of Key Insights
    11.2 The Impact of ANNs on Future Pollution Modelling and Policy

12. References

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