Computational Automation for Water Security

Enhancing Water Quality Management

1st Edition - February 27, 2025
Imprint: Elsevier
Editors: Ashutosh Kumar Dubey, Arun Lal Srivastav, Abhishek Kumar, Fausto Pedro Garcia Marquez, Dimitrios A Giannakoudakis
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 3 3 2 1 - 7
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 3 3 2 2 - 4

Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and ou… Read more

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Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes. Through the coverage of advanced computational techniques, such as data analysis, machine learning, and optimization strategies, readers will gain a deep understanding of how computational automation can revolutionize decision-making. This book is an invaluable resource for professionals, researchers, and policymakers seeking to stay at the forefront of water quality management practices, harnessing the power of computational automation for a cleaner, healthier future.

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of contributors
About the editors
Preface
Chapter 1. Artificial intelligence and machine learning based water quality monitoring, prediction, and analysis: a comprehensive review
Abstract
1.1 Introduction
1.2 Literature survey
1.3 Discussions
1.4 Conclusion and future work
References
Chapter 2. Advancements in water quality monitoring: leveraging machine learning and artificial intelligence for environmental management
Abstract
2.1 Introduction
2.2 Literature survey
2.3 Discussions
2.4 Conclusion
References
Chapter 3. Revolutionizing water quality management the impact of machine learning and artificial intelligence
Abstract
3.1 Introduction to machine learningand artificial intelligence in water quality monitoring
3.2 Overview of machine learning and artificial intelligence technologies
3.3 Revolutionizing water management
3.4 Automation and accuracy in monitoring processes
3.5 Future predictive capabilities of water quality
3.6 Enhancing efficiency and resource management
3.7 Applications of machine learning and artificial intelligence in automating water quality monitoring, analysis, and management
3.8 Discussion
3.9 Conclusion: toward global water security and sustainability
References
Chapter 4. Enhancing water quality management: the role of predictive modeling and IoT in monitoring, analysis, and intervention
Abstract
4.1 Introduction
4.2 Developing a machine learning model for assessing and monitoring water quality parameters
4.3 Strategies for data acquisition: remote sensing, Internet of Things, and unmanned aerial vehicles
4.4 Applications of machine learning and artificial intelligence in water quality assessment
4.5 Real-life applications of artificial intelligence in water quality management
4.6 Discussion
4.7 Conclusions
References
Chapter 5. Enhancing water quality management through artificial intelligence and machine learning technologies
Abstract
5.1 Introduction
5.2 Major artificial intelligence techniques used in water quality analysis, monitoring, and management
5.3 Prediction of various water quality parameters
5.4 Discussion
5.5 Conclusion
References
Chapter 6. Machine learning and artificial intelligence application in automotive water quality monitoring, analysis, and management
Abstract
Nomenclature
6.1 Introduction
6.2 Discussion section
6.3 Conclusion
References
Chapter 7. Integrating AI, machine learning, and nanotechnology: shaping the future of water quality management
Abstract
7.1 Introduction
7.2 Fundamentals of water quality management
7.3 Role of AI in water quality management
7.4 Application of machine learning technology in water quality management
7.5 Nanotechnology for water quality management
7.6 Integration of AI, machine technology, and nanotechnology in water quality management
7.7 Discussion
7.8 Conclusion
References
Further reading
Chapter 8. Empowering water security: the synergy of automation and IoT integration
Abstract
8.1 Introduction
8.2 The synergy of automation and IoT
8.3 Enhanced infrastructure resilience
8.4 Future prospects and innovations
8.5 Conclusion and recommendations
References
Chapter 9. Cost-benefit analysis of automated water quality management: investment, savings, and strategic advantages
Abstract
9.1 Introduction
9.2 Literature review
9.3 Initial investment and operational saving costs
9.4 Discussion
9.5 Conclusion
References
Further reading
Chapter 10. Significance of automation in water treatment processes
Abstract
10.1 Introduction
10.2 Various automation used in water treatment processes
10.3 Process automation used in wastewater treatment
10.4 Importance of automation in water treatment plants
10.5 Discussion
10.6 Conclusion
References
Chapter 11. Water sustainability: a review of advances in water quality management technologies
Abstract
11.1 Introduction
11.2 Methodology
11.3 IoT and ML in WQM
11.4 Water sustainability, WQM, UN SDGs
11.5 Discussion
11.6 Conclusions
References
Chapter 12. Future directions in water quality management: integrating advanced technologies and sustainable practices
Abstract
12.1 Introduction
12.2 Concept of water quality management and present technical scenario
12.3 Data analytics and predictive modeling
12.4 Decentralized water treatment systems and its significance
12.5 Emerging contaminant removal technique
12.6 Resource recovery and circular economy
12.7 Discussion
12.8 Policy and governance innovations
12.9 Conclusion
References
Chapter 13. Future trends and emerging technologies in water quality management
Abstract
13.1 Introduction
13.2 Innovations and sustainable practices in water treatment technologies
13.3 Nanotechnology-based solutions
13.4 Membrane filtration advancements
13.5 Electrochemical water treatment methods
13.6 Nature-based solutions (constructed wetlands, biofilters)
13.7 Wastewater recycling and reuse
13.8 Low-energy and renewable energy technologies in water treatment
13.9 Emerging technologies in water quality monitoring
13.10 Real-time monitoring systems
13.11 Future trends in water quality management
13.12 Discussion
13.13 Conclusions
References
Chapter 14. Addressing complex challenges in water quality management: emerging technologies and sustainable strategies
Abstract
14.1 Overview of water quality
14.2 The water-energy nexus
14.3 Current challenges in water quality management
14.4 Emerging trends in water quality management
14.5 Sustainable approaches in water quality management
14.6 Artificial intelligence and machine learning applications in water quality management
14.7 Advanced sensor technologies for monitoring water quality
14.8 Discussion
14.9 Conclusion
14.10 Future perspective (recommendation)
References
Chapter 15. Introduction to computational automation in water quality management
Abstract
Abbreviations
15.1 Introduction
15.2 Historical background
15.3 Role of computational automation in modern water quality management
15.4 Emergence of automation in water quality
15.5 Technologies in computational automation
15.6 Software and systems for water quality management
15.7 Automation techniques and methodologies
15.8 Case studies and industrial applications
15.9 Challenges and limitations
15.10 Future directions and innovations
15.11 Conclusion
Acknowledgments
References
Chapter 16. Case studies: Successful implementation of automation for water quality assessment
Abstract
16.1 Introduction
16.2 Literature review
16.3 Case study 1
16.4 Case study 2: future trends in automated water quality assessment
16.5 Case study 3: farm runoff and mitigation
16.6 Regional weather prediction models
16.7 Financial and environmental effects of the use of automation
16.8 Summary and improvements
16.9 Conclusion (which includes shortcomings from what was investigated and further ideas)
16.10 Conclusion
References
Chapter 17. Enhancing water security through automation: case studies and technical advancements in water quality management
Abstract
17.1 Introduction
17.2 The rationale for adopting automation in water quality assessment
17.3 Fundamentals of computational automation
17.4 Definition and principles of computational automation
17.5 Role of computational methods in water quality assessment
17.6 Case study 1: automated sensor networks for real-time monitoring
17.7 Analysis of the effectiveness of real-time data collection in identifying pollution sources and assessing water quality trends
17.8 Case study 2: exploration of a case study employing machine learning algorithms to develop predictive models for water quality assessment
17.9 Considerations for scalability and integration with existing monitoring frameworks
17.10 Identification of challenges encountered in implementing automation for water quality assessment
17.11 Strategies for addressing challenges and maximizing the benefits of automation
17.12 Future directions and opportunities
17.13 Literature review
17.14 Key findings, interpretations, and implications
17.15 Conclusion
References
Chapter 18. Jal Jeevan Mission and role of smart technologies in achieving water security in rural India
Abstract
18.1 Introduction
18.2 Global water status
18.3 Multifarious nexus of water security in India
18.4 Institutionalization of water security in changing climate in India
18.5 Importance of smart technology in attaining water security through water management
18.6 Critical discussion
18.7 Conclusion
References
Chapter 19. Computational automation for enhancing water security and quality management
Abstract
19.1 Introduction
19.2 Technical challenges and integration with existing infrastructure
19.3 Financial challenges
19.4 Societal challenges
19.5 Ethical considerations in water automation
19.6 Addressing the challenges: technical solutions
19.7 Financial strategies for overcoming water automation challenges
19.8 Societal approaches to addressing challenges in water automation
19.9 Discussion
19.10 Future directions and recommendations
19.11 Conclusion
References
Chapter 20. Synergizing sustainability: integrating computational automation for water security and sustainable tourism
Abstract
20.1 Introduction
20.2 Literature review
20.3 Integrating frameworks
20.4 New framework developed: integrated computational sustainability framework for water security and tourism
20.5 Discussion and conclusion
References
Chapter 21. The moral current: ethical dilemmas in water automation
Abstract
21.1 Introduction
21.2 Literature review
21.3 Ethical considerations in AI-driven agriculture
21.4 Ethical conundrums in water automation with artificial intelligence
21.5 Challenges and future prospects
21.6 Discussion
21.7 Conclusion
References
Chapter 22. Applications of machine learning and artificial intelligence in automating water quality monitoring, analysis, and management
Abstract
22.1 Introduction
22.2 Data acquisition and preprocessing
22.3 Machine learning for water quality analysis
22.4 AI-driven automation systems
22.5 Applications and case studies
22.6 Challenges and future directions
22.7 Conclusion
References
Index

Ashutosh Kumar Dubey

Ashutosh Kumar Dubey is an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Himachal Pradesh, India. He is also a Postdoctoral Fellow of the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Ciudad Real, Spain.

Affiliations and expertise

Department of Computer Science and Engineering, Institute of Engineering and Technology, Chitkara University, India

Arun Lal Srivastav

Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.

Affiliations and expertise

Chitkara University, Himachal Pradesh, Solan, India

Abhishek Kumar

Dr. Abhishek Kumar is a professor and post-doctorate fellow in computer science at Ingenium Research Group, based at Universidad De Castilla-La Mancha in Spain. He has been teaching in academia for more than 8 years, and published more than 50 articles in reputed, peer reviewed national and international journals, books, and conferences. His research area includes artificial intelligence, image processing, computer vision, data mining, and machine learning.

Affiliations and expertise

Professor, Post-doctorate Fellow, Ingenium Research Group, Universidad De Castilla-La Mancha, Spain

Fausto Pedro Garcia Marquez

Fausto Pedro García Márquez works as a Professor and as Director of the Ingenium Research Group at the Universidad De Castilla-La Mancha, Spain. He is an Honorary Senior Research Fellow at Birmingham University, UK, and a Lecturer at the Postgraduate European Institute. He has published more than 150 papers and 31 books (Elsevier, Springer, Pearson, McGraw-Hill, Intech, IGI, Marcombo, AlfaOmega). He has been Principal Investigator in 4 European projects, 6 national projects, and more than 150 projects for universities, companies, and other institutions. His main interests are: Artificial Intelligence, Maintenance, Management, Renewable Energy, Transport, Advanced Analytics, and Data Science.

Affiliations and expertise

Professor, Universidad De Castilla-La Mancha, Spain

Dimitrios A Giannakoudakis

Dr. Dimitrios Giannakoudakis graduated as chemist from Aristotle University of Thessaloniki (AUTh) in Greece, where he obtained also two M.Sc. degrees (in Physical-chemistry & Electrochemistry and in New Educational Technologies). He received his PhD degree on “Nanotechnology and Materials Chemistry” from the City University of New York (CUNY) with full scholarship, in 02/2017. Then, he continued as postdoctoral-fellow and adjunct tutor at the City College of New York, AUTh, and the Institute of Physical Chemistry (IChF) of Polish Academy of Sciences. Afterwards, he served at IChF as adj. Assistant Professor and tutor of “Modern nano-Topics in Physical Chemistry”. Currently, he is Research Associate and tutor at AUTh. He has co-authored more than 105 publications in leading peer-reviewed journals (avg. Impact Factor as 1st author above 11), with the articles to be cited more than 3000 times, one monograph by Springer, three edited books by Elsevier, more than 20 book chapters, and 3 invention patents. For more details: www.DaGchem.com

Affiliations and expertise

Assistant Professor, Institute of Physical Chemistry of Polish Academy of Sciences, Greece

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Computational Automation for Water Security

Enhancing Water Quality Management

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Ashutosh Kumar Dubey

Arun Lal Srivastav

Abhishek Kumar

Fausto Pedro Garcia Marquez

Dimitrios A Giannakoudakis

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