Handbook of Digitalization and Big Data in the Water Sector
CRC Press
978-1-041-24533-9 (ISBN)
- Noch nicht erschienen (ca. Mai 2026)
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
With special emphasis on the integration of big data, artificial intelligence (AI), and machine learning (ML), Volume 1 provides theoretical foundational and practical insights for water systems optimization, resource conservation, and sustainable operations; Volume 2 explores pathways to a sustainable, low-carbon future by emphasizing predictive maintenance, energy-efficient operations, leak detection, and climate-adaptive planning; and Volume 3 pursues real-world applications in water supply, wastewater treatment and flood management through a host of diverse and well-researched case studies.
Ideal for water engineers, researchers, policymakers, and sustainability practitioners, this handbook serves as an essential guide for professionals seeking to harness digitalization for smarter, data-driven water management. It is equally valuable for graduate students, academics, and technology innovators interested in bridging the gap between emerging AI capabilities and practical water sector applications.
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.
Volume 1 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
Volume 2 1. Supply Mapping for a Sustainable Future: Data-Driven Efforts in Decision Making 2. Digital Approaches in Water Quality Management: Transitioning Conventional Techniques to Digitalisation 3. Sustainability of the Water Sector Using IoT 4. IoT-based Water Quality Monitoring 5. The way forward of digitalization in water sector 6. Role of Big Data in projecting water scarcity and drought 7. Big data-driven of ecological protection 8. Promoting Sustainable Agriculture with Drones in Rural Areas: Socio-Cultural Requirements and Considerations 9. Digital Twins as a Transformative Framework for Intelligent Water and Wastewater Management 10. Integration of Digital Twins with AI Tools 11. Challenges and Opportunities for Adopting Digital Twins in The Water Treatment Industry 12. Emerging Opportunities and Threats in the Digital Revolution in the Water Sector 13. Big data to help water sector become carbon neutral 14. Water supply mapping for sustainable future, data-driven efforts in decision making
vol 3
Volume 3 1. The role of big data and AI policy in big data during the earthquake Using the k-means algorithm for defining the seismic input 2. A Lightweight Deep Learning LSTM Model for Efficient Real-time Water Quality Classification in IoT-Enabled Aquaculture 3. Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants: A case study from China 4. Can Indonesian Maritime Education Harness Big Data to Meet the Belt and Road Initiative’s Demands? 5. Unlocking the Potential of Big Earth Data to Track Water Scarcity 6. AI-Driven Intelligent Water Systems: A New Paradigm for Water Resource Management 7. Water Conservation and AI 8. Water Banking as a Management and Conservation Strategy for a Vital Resource 9. AI-Integrated Drone Systems for Illegal Waste Monitoring in Indonesia: Enhancing Environmental Compliance and Data-Driven Waste Management 10. The trajectory of digital transformation within the water sector 11. Artificial neural network (ANN) modelling of wastewater effluent treatment: A case study from Asia region 12. Conventions on International Water, the Abuse of Transboundary
| Erscheint lt. Verlag | 15.5.2026 |
|---|---|
| Verlagsort | London |
| Sprache | englisch |
| Maße | 174 x 246 mm |
| Themenwelt | Naturwissenschaften ► Geowissenschaften ► Hydrologie / Ozeanografie |
| Technik ► Bauwesen | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-041-24533-5 / 1041245335 |
| ISBN-13 | 978-1-041-24533-9 / 9781041245339 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
