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.
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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.
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Hardcover. Zustand: new. Hardcover. 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. With special emphasis on the integration of big data, artificial intelligence (AI), and machine learning (ML), Volume 2 explores pathways to a sustainable, low-carbon future by emphasizing predictive maintenance, energy-efficient operations, leak detection, and climate-adaptive planning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781032880679
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