This is an extensive and comprehensive book, showcasing the applications of machine learning and artificial intelligence in environmental monitoring of the quality of air, soil and water, prediction of environmental risks, optimisation and prediction of remediation processes and enhancement of resource management.
Harnessing Machine Learning for Environmental Restoration and Surveillance is an extensive and comprehensive book, showcasing the applications of machine learning and artificial intelligence in environmental monitoring of the quality of air, soil and water, prediction of environmental risks, optimisation and prediction of remediation processes and enhancement of resource management.
The book offers readers an opportunity to understand how contemporary smart technology (machine learning and artificial intelligence) can be used to safeguard and improve our environment as well as to solve environmental problems and provide sustainable solutions. It offers insight and an opportunity to evaluate theoretical concepts and real-world case studies, thereby boosting innovation and collaboration. The authors give modest explanations of how artificial intelligence and machine learning can monitor pollution, forecast environmental hazards, and support the optimisation process for the effective removal of environmental contaminants. Real-world examples where these advanced smart technology tools have been successfully utilised are also presented in this book.
The content and coverage of this academic text are ideal for undergraduate and postgraduate students, researchers, professionals in environmental science, data science, and engineering, as well as individuals interested in the environment and technology.
1. Introduction to Environmental Challenges and Technological Solutions
2. Machine Learning Fundamentals for Environmental Monitoring and Remediation
3. Machine Learning-Driven Environmental Monitoring Systems in Air, Water,
and Soil
4. Machine Learning in Pollution Detection and Control in Air,
Water, and Soil
5. Machine Learning Predictive Modeling in Environmental
Forecasting
6. Future Directions and Innovation on Emerging Machine Learning
Technologies in Environmental Science. Index
Robert Birundu Onyancha is a Senior Lecturer of Physics at the Technical University of Kenya; a Research Fellow at the College of Graduate Studies, School of Interdisciplinary Research and Graduate Studies, University of South Africa; and a Senior Lecturer of Physics on sabbatical at the Department of Physics, School of Pure and Applied Science, Kisii University, Kenya.
Kingsley Eghonghon Ukhurebor is a Senior Lecturer/Researcher (due for an Associate Professor) and acting Director, Centre for Open and Distance Learning and former acting Head of the Department of Physics at Edo State University Iyamho, Nigeria. He is also a Research Fellow at WASCAL, Competence Centre, Ouagadougou, Burkina Faso. His research interests include applied physics, climate physics, environmental physics, telecommunications physics, and materials science (nanotechnology). He is currently ranked among the top 50 authors in Nigeria by Scopus scholarly output and is listed among the top 2% of scientists in the world by Stanford University, USA, and Elsevier.
Uyiosa Osagie Aigbe is a former Research Fellow with the Department of Mathematics and Physics, Faculty of Applied Science, Cape Peninsula University of Technology, Cape Town, South Africa. He was also an Ad-Hoc Research Fellow with the Centre for Space Research and the National Institute of Theoretical and Computational Science, North West University, Potchefstroom, South Africa. He is currently a teaching assistant with the Discipline of Physics, School of Agriculture & Science, College of Agriculture, Engineering & Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.. His research interests are in applied physics, nanotechnology, fluid dynamics, water purification processes, image processing, environmental physics, machine learning, statistical analysis, and materials science.
