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 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.
Dr. Amit Awasthi is an Assistant Professor at the University of Petroleum and Energy Studies, Dehradun, India, teaching and doing research in the areas of Atmospheric and Environment Sciences, Aerosol Technology and Measurements, Air Monitoring, and Climate Change. He has published 4 books and 40 research papers with a total Impact factor of ~100, an h-index of 16, and a total of 1050 citations. He received his Ph. D from Thapar University, Patiala in 2011. Dr. Kanhu Charan Pattnayak is a Senior Climate Impact Scientist at the National Environmental Agency, Singapore with over 17 years of experience in climate impact research and climate modeling. He has a Ph.D. in Climate Science from the Indian Institute of Technology Delhi and has held research positions at the University of Leeds, the National Environment Agency of Singapore, and the NCMRWF. He has published over 25 research papers in top tier journals and has served as a reviewer for the Sixth Assessment Report of the United Nations Intergovernmental Panel on Climate Change. Dr. Gaurav Dhiman is an Assistant Professor in the School of Sciences and Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Patiala, Punjab, India. He holds a Ph.D. in Computer Engineering from Thapar Institute of Engineering & Technology, Patiala. He is recognized as one of the world's top researchers by Stanford University's list of the world's top 2% scientists prepared by Elsevier and the top 1% as a highly cited researcher by Clarivate Analytics. He is a Senior Member of IEEE. He has authored over 300 peer-reviewed research papers and 10 books. He is currently serving as a guest editor for more than forty special issues in various peer-reviewed journals. He is an Editor-in-Chief of the International Journal of Modern Research (IJMORE). He is an Associate Editor of IEEE Transactions on Industrial Informatics, IET Software (Wiley), Expert Systems (Wiley), IEEE Systems, Man, and Cybernetics Magazine, IEEE Transactions on Consumer Electronics, and more. Dr. Pushp Raj Tiwari is a climate scientist and Fellow of UK's Higher Education Academy (FHEA), specializes in climate change, big data and earth system modelling. A former RCUK-ECR Fellow, he now leads research group on Climate Change Modelling and Applications. His work focuses on aerosol-cloud-climate interaction and reducing the associated uncertainties related to them in climate models.
Inhaltsangabe
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.
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.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
