Handbook of Digitalization and Big Data in the Water Sector

Tonni Agustiono Kurniawan is a recognized global leader in tackling complex environmental problems that have significant societal relevance and positive impact in the world. His focus on sustained scientific research is evident from more than 355 journal articles, 25 articles in conference proceedings, twelve monographs, and 28 book chapters. To date, Kurniawan is the first author of 15% of the works with an h-index of 68 and citations of over 19,500 counts (Scopus), while being the corresponding author of one third of the same works. The scientific contributions are tangible manifestations of his competence and research impact in the discipline.Abdelkader Anouzla received his Ph.D. in Science & Technology at Hassan II University -Faculty of Science and Technology Mohammedia, specializing in water treatment. Dr. Abdelkader Anouzla has published almost 100 peer-reviewed articles and twenty books. He has been invited as a guest speaker to several conferences and has also published his research in numerous proceedings. His research interests include water and waste treatment, wastewater treatment plant operation, leachate discharge treatment, solid waste sorting, technical landfill management, composting of solid waste and sludge from wastewater treatment plants, water-food-energy nexus, microplastic pollution, digitalization in the water sector and nitrogen pollution.

• 1. Harnessing machine learning in the water sector to accelerate sustainable development goals (SDGs) 2. Unlocking the potential of AI in the water sector 3. The applications of Internet of Things in water sector: Taxonomy, Use cases, Key challenges and Future Road map 4. Deployment of Artificial Intelligence and Satellite to Promote Sustainable Cities 5. Role of AI Policy in Responding to Climate Change and Mitigating the Food and Energy Crisis 6. AI- based modeling for predicting the disinfection by-products in water 7. Big data in support of carbon neutrality in water sector 8. Using satellite remote sensing monitoring in boosting water resource substitutability in agriculture 9. AI in wastewater treatment applications 10. Strengthening Machine Learning Reproducibility to Ensure Water Security in the Long Term 11. Machine Learning Application Based on Big Data for Prediction of Wastewater Quality 12. AI Success in Water Management 13. Application of machine learning techniques predicated on extensive datasets for the forecasting of wastewater quality 14. Predicting future trends in the integration of AI and water management