About the Book
This book gives readers insight into the state-of-the-art use of artificial intelligence for the environment. It encompasses most of the significant facets of current breakthroughs in the fields of conceptions, methodologies, resources, and leading artificial intelligence solutions for the environment. This book presents research at the forefront on applications of artificial intelligence in combating climate change, natural hazards, and textile dyeing pollution (water pollution), for forecasting, assessing air quality trends, and air pollution monitoring. It explains how machine learning can prove to be an efficient technique to forecast the consumption of energy and how AI can be effective for renewable energy systems. Research in this book widens its scope to present the problems, opportunities, and directives for the application of AI systems in engine exhaust prediction. One of the new and interesting things explored is to provide and predict the rate of decay of human lung tissue (due to Particulate Matter exposure) with the help of AI in this book. Likewise, the book opens its scope to various environmental problems and focuses on giving the best solutions with an application of artificial intelligence; this feature makes this book an indispensable guide for environmental scientists and AI researchers of all levels. The book is written comprehensively so that engineering professionals, programmers, environmentalists, graduates, postgraduates, and researchers from beginning/intermediate level to advance level can be enlightened.
Table of Contents:
Chapter 1. AI for Forecasting, Assessing Air Quality Trends, and Air Pollution Monitoring.- Chapter 2. Artificial Intelligence for Impeding the Impending Climate Change.- Chapter 3. Artificial Intelligence Curtailments of Environmental impacts of Renewable Energy Systems: Solar, Wind, Hydro, Biomass, and Geothermal.- Chapter 4. Artificial Intelligence for Apter Waste Management Encapsulating Environmental impacts and health hazards associated with open dumping of organic waste.- Chapter 5. Applications of AI in Assessing Threats of Catastrophic Natural Hazards.- Chapter 6. Review of Experimental investigations on exposure of lung tissue with fine and ultrafine particles and possible implications of AI. Chapter 7. Review on Recycling of Platinum Group Metals, their demand and supply prediction using AI, And Alternative Catalysts For Catalytic Converters.- Chapter 8. Machine Learning in Renewable Energy Systems.- Chapter 9. Artificial Intelligence and its Potential Applications in Identifying Textile Dyeing Pollution in Bangladesh.- Chapter 10. Problems, Opportunities, and Directives for the Application of AI systems in Engine Exhaust Prediction.
About the Author :
Dr. Ajitanshu Vedrtnam has two decades of research and teaching experience. He has completed BE (Mechanical Engineering), MBA (Marketing), ME (Manufacturing)-Gold Medallist, GATE (ME), and Ph.D. (Applied Mechanics). Dr. Vedrtnam is the recipient of prestigious fellowships including Marie-Curie grants, Pacific fellowship, post-doctoral fellowships from University of California, USA, National University of Ireland, Ireland, Polish Academy of Sciences, Poland, Slovak Academy of Sciences, Slovakia, Singapore Research Foundation, Singapore, Technion Israel Institute of Technology, Israel, Research fellowship from Institute of Advanced Materials, Sweden, Research Fellowship from Ministry of HRD, Govt. of India. Dr. Vedrtnam has secured Dr. Kevin Wyche is a reader in Atmospheric Science within the School of Applied Sciences (SAS) and the director of the University of Brighton's Center for Earth Observation Science (CEObS). He is also lead of the SAS Environment and Public Health Research and Enterprise Group (EPHREG) and founder and Principal Investigator of Air Environment Research (AER) and the Brighton Atmospheric Observatory (BAO), a state-of-the-art, highly instrumented real-time tropospheric observation laboratory. His research interests are based around the study of the Earth's atmosphere, with focus on tropospheric chemistry and air pollution. More specifically, his interdisciplinary research falls into the areas of (i) fundamental chemical processes controlling tropospheric composition and change, (ii) air quality science, and (iii) analytical instrument development and application in Earth Observation. Mr. Balendra VS Chauhan is a Ph.D. Research scholar at the Center for Earth Observation Science, School of Applied Sciences, University of Brighton, UK. He is working on environmental research based on finding the effect of ultrafine particles on human health. He is incorporating artificial intelligence in the environmental research. His research areas are i) applications of artificial intelligence in environmental pollution mitigation, ii) experimental analysis on exposure of lung tissue with ultrafine particles, iii) engine emission and performance testing using alternative fuels, iv) and motion (Sedimentation) of microparticles in fluids. Currently, he is also working as a Science and Engineering lecturer at Kaplan International College London, UK. He has an experience of working as a Young Scientist at the Institute of Fundamental and Technological Research, Polish Academy of Sciences, Poland. Dr. Sneha Verma is a Ph.D. researcher at the School of Engineering and Technology at City, University of London, UK. She has received her M.Phil. in 2020 from City University of London. She has completed her Bachelor's and Master's in the year of 2015 and 2017, respectively, from India. She is also a recipient of prestigious Doctoral fellowship at City University of London to server her Ph.D. degrees. She also received the status of Associate Fellow (AFHEA) against the UK Professional Standards Framework. Worshipful Company of Scientific Instrument Makers awarded her post-grad award for her excellent research work. Her research interests include the application of nanotechnology, finite element method modeling, gold nanoantennas, surface plasmons in artificial intelligence, and machine learning. Currently, she is developing an artificial intelligence or machine learning techniques for simulation and optimization of complex photonic nanostructures.
