Water Quality Monitoring and Management

  • ID: 4519347
  • Book
  • 352 Pages
  • Elsevier Science and Technology
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Water Quality Monitoring and Management: Basis, Technology and Case Studies presents recent innovations in operations management for water quality monitoring. It highlights the cost of using and choosing smart sensors with advanced engineering approaches that have been applied in water quality monitoring management, including area coverage planning and sequential scheduling. In parallel, the book covers newly introduced technologies like bulk data handling techniques, IoT of agriculture, and compliance with environmental considerations. Presented from a system engineering perspective, the book includes aspects on advanced optimization, system and platform, Wireless Sensor Network, selection of river water quality, groundwater quality detection, and more.

It will be an ideal resource for students, researchers and those working daily in agriculture who must maintain acceptable water quality.

  • Discusses field operations research and application in water science
  • Includes detection methods and case analysis for water quality management
  • Encompasses rivers, lakes, seas and groundwater
  • Covers water for agriculture, aquaculture, drinking and industrial uses
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Part 1. Introduction of the sensors in water quality monitoring Part 2. Wireless Sensor Network in Water Quality Monitoring Part 3. System and platform for water quality monitoring Part 4. Water quality evaluation Part 5. Prediction of Water Quality Part 6. Water Quality Early Warning Part 7. Detection of river water quality Part 8. Water Quality Detection for Lakes Part 9. sea water quality detection Part 10. Drinking Water Detection Part 11. Groundwater quality detection Part 12. Water Quality monitoring in aquaculture Part 13. Detection of industrial water quality

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Li, Daoliang
College of Information and Electrical Engineering, China Agricultural University, China.

The author is well-recognized internationally, holding executive positions in the key international associations promoting agricultural engineering. He has long experience in teaching related courses including:

Master level and Ph.D. level courses in intelligent information system of agriculture, Systems Engineering, Agricultural advanced sensing, intelligent information processing, Internet of things of agriculture, System analysis and design, Information technologies and Undergraduate courses in Information technology in Agriculture, agriculture expert systems.
Liu, Shuangyin
S. Liu received his B.E. degree in the Department of Computer Science, Air Force Engineering University in 2002 and his M.E. degree in Faculty of Computer, Guangdong University of Technology in 2006. He is a Ph.D. student in the College of Information and Electric Engineering of China Agriculture University. He is a lecturer in the College of Information, Guangdong Ocean University, and he is a member of China Computer Federation. He primary research interests are intelligent information system of agriculture, artificial intelligence, software engineering, computational intelligence.

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