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Full Description
This book is a comprehensive overview of advancements in artificial intelligence (AI) and how it can be applied in the field of air quality management. It explains the linkage between conventional approaches used in air quality monitoring and AI techniques such as data collection and preprocessing, deep learning, machine vision, natural language processing, and ensemble methods. The integration of climate models and AI enables readers to understand the relationship between air quality and climate change. Different case studies demonstrate the application of various air monitoring and prediction methodologies and their effectiveness in addressing real-world air quality challenges.
Features
A thorough coverage of air quality monitoring and prediction techniques.
In-depth evaluation of cutting-edge AI techniques such as machine learning and deep learning.
Diverse global perspectives and approaches in air quality monitoring and prediction.
Practical insights and real-world case studies from different monitoring and prediction techniques.
Future directions and emerging trends in AI-driven air quality monitoring.
This is a great resource for professionals, researchers, and students interested in air quality management and control in the fields of environmental science and engineering, atmospheric science and meteorology, data science, and AI.
Contents
1. Air Quality Monitoring (AQM) and Prediction: Transitioning from Conventional to AI Techniques. 2. Temporal Variations of Sulphur Dioxide Levels across India: A Biennial Assessment (2020-2021). 3. The Effectiveness of Machine Learning Techniques in Enhancing Air Quality Prediction. 4. Enhancing Environmental Resilience: Precision in Air Quality Monitoring through AI-Driven Real-Time Systems. 5. Forecasting Air Pollution with Artificial Intelligence: Recent Advancements at Global Scale and Future Perspectives. 6. Integrating AI into Air Quality Monitoring: Precision and Progress. 7. Application of AI-based Tools in Air Pollution Study. 8. Study of Extreme Weather Events in the Central Himalayan Region through Machine Learning and Artificial Intelligence: A Case Study. 9. Machine Learning Applications in Air Quality Management and Policies. 10. A Glimpse into Tomorrow's Air: Leveraging PM 2.5 with FP Prophet as a Forecasting Model. 11. Air Quality Forecast using Machine Learning Algorithms. 12. Deep Learning Approaches in Air Quality Prediction. 13. Incorporation of AI with Conventional Monitoring Systems. 14. A Comparative Evaluation of AI-Based Methods and Traditional Approaches for Air Quality Monitoring: Analyzing Pros and Cons. 15. ML Driven Hydrogen Yield Prediction for Sustainable Environment.
