Harnessing Machine Learning for Environmental Restoration and Surveillance

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, and is currently an Ad-Hoc Research Fellow with the Centre for Space Research and the National Institute of Theoretical and Computational Science. His research interests are in applied physics, nanotechnology, fluid dynamics, water purification processes, image processing, environmental physics, machine learning, statistical analysis, and materials science.

• 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