This 3-volume set delves into the intersection of cutting-edge technology and environmental science to address pressing challenges in the water sector and develop digitalization solutions that can have practical implementation in decision making and management of wastewater treatment.
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.
This 3-volume set delves into the intersection of cutting-edge technology and environmental science to address pressing challenges in the water sector and develop digitalization solutions that can have practical implementation in decision making and management of wastewater treatment.
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
Initiatives 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
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.
