Handbook of Digitalization and Big Data in the Water Sector

Nyhet

Role of Artificial Intelligence and Machine Learning (Volume 1)

Inbunden, Engelska, 2026

AvTonni Agustiono Kurniawan,Abdelkader Anouzla,Skudai) Kurniawan, Tonni Agustiono (University Teknologi Malaysia

3 269 kr

Kommande

This three-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.

Produktinformation

Utgivningsdatum2026-04-08
Mått174 x 246 x undefined mm
Vikt453 g
FormatInbunden
SpråkEngelska
Antal sidor264
FörlagTaylor & Francis Ltd
ISBN9781032880310

Tillhör följande kategorier

Energiindustri inom Ekonomi och ledarskap
Hydrologi inom Naturvetenskap och teknik
Byggnadsteknik inom Naturvetenskap och teknik

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, 12 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 earned his Ph.D. in Science and Technology from Hassan II University – Faculty of Science and Technology Mohammedia, specializing in water treatment. He has published almost 100 peer-reviewed articles and 20 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