Computational Automation for Water Security

Enhancing Water Quality Management

Ashutosh Kumar Dubey, Arun Lal Srivastav, Abhishek Kumar, Fausto Pedro Garcia Marquez, Dimitrios A Giannakoudakis

• Publisher: Elsevier
• Year: 2025
• eBook ISBN:
  9780443333224
• Topic: Physical Sciences
• Subtopic: Geophysics

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Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes.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. - Offers a holistic understanding of the application of computational automation in water quality management - Contains practical and unique updates to help learners how to apply computational techniques to address water quality challenges - Provides a comprehensive and multidisciplinary perspective on water quality management

Table of contents

• Title of Book
• Chapter 1. Artificial intelligence and machine learning based water quality monitoring, prediction, and analysis: a comprehensive review
• Chapter 2. Advancements in water quality monitoring: leveraging machine learning and artificial intelligence for environmental management
• Chapter 3. Revolutionizing water quality management the impact of machine learning and artificial intelligence
• Chapter 4. Enhancing water quality management: the role of predictive modeling and IoT in monitoring, analysis, and intervention
• Chapter 5. Enhancing water quality management through artificial intelligence and machine learning technologies
• Chapter 6. Machine learning and artificial intelligence application in automotive water quality monitoring, analysis, and management
• Chapter 7. Integrating AI, machine learning, and nanotechnology: shaping the future of water quality management
• Chapter 8. Empowering water security: the synergy of automation and IoT integration
• Chapter 9. Cost-benefit analysis of automated water quality management: investment, savings, and strategic advantages
• Chapter 10. Significance of automation in water treatment processes
• Chapter 11. Water sustainability: a review of advances in water quality management technologies
• Chapter 12. Future directions in water quality management: integrating advanced technologies and sustainable practices
• Chapter 13. Future trends and emerging technologies in water quality management
• Chapter 14. Addressing complex challenges in water quality management: emerging technologies and sustainable strategies
• Chapter 15. Introduction to computational automation in water quality management
• Chapter 16. Case studies: Successful implementation of automation for water quality assessment
• Chapter 17. Enhancing water security through automation: case studies and technical advancements in water quality management
• Chapter 18. Jal Jeevan Mission and role of smart technologies in achieving water security in rural India
• Chapter 19. Computational automation for enhancing water security and quality management
• Chapter 20. Synergizing sustainability: integrating computational automation for water security and sustainable tourism
• Chapter 21. The moral current: ethical dilemmas in water automation
• Chapter 22. Applications of machine learning and artificial intelligence in automating water quality monitoring, analysis, and management
• Index