Artificial Intelligence for Air Quality Monitoring and Prediction [Hardcover]

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.

This book is a comprehensive overview of advancements in artificial intelligence (AI) and how it can be applied in air quality management. It explains the linkage between conventional approaches used in air quality monitoring with AI techniques such as data collection, preprocessing, deep learning, machine vision, ensemble methods, and more.

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 collelãC