Artificial Intelligence and Data Science in Environmental Sensing

Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.

Cover image
Title page
Table of Contents
Copyright
Contributors
Editor Bio
Preface
Chapter 1. Smart sensing technologies for wastewater treatment plants
1. Introduction
2. Online estimation
3. Fault detection and diagnostics
4. Multivariate analysis models
5. Conclusion and future direction
Chapter 2. Advancements and artificial intelligence approaches in antennas for environmental sensing
1. Printed antennas for wireless sensor networks
2. Printed antenna sensors for material characterization
3. Epidermal antenna for unobtrusive human-centric wireless communications and sensing
4. Artificial intelligence in antenna design
Chapter 3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport
1. Introduction
2. The role of transport in the economy and environment
3. Geo-sensing; evolution in the geography
4. Geographic Information System as a revolution or/and an evolution
5. Geo-sensing for moving toward eco-routing and low-emission transport
6. Intelligent geo-sensing and AI as a new window to the future
7. Conclusion
Chapter 4. Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization
1. Introduction
2. Strategy of response surface methodology
3. Practical application of RSM in electrochemical processes for wastewater treatment
4. Merits and demerits of RSM
5. Conclusions
Chapter 5. Artificial intelligence and sustainability: solutions to social and environmental challenges
1. Introduction
2. AI and social change: the case of food and garden waste management
3. AI and ecosystem services: insights into bushfire management and renewable energy production
4. Challenges of using AI to achieve sustainability
5. Implications and conclusion
Chapter 6. Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines
1. Decision-making in renewable energy investments
2. Decision-making tools on the development and design of offshore wind power farms
3. Background of multiattribute decision-making tools
4. Background of multiobjective problems in offshore and wind farms
Chapter 7. Recent advances of image processing techniques in agriculture
1. Introduction
2. Application in plants detection
3. Application in livestock recognition
4. Application in fruits and vegetables recognition
5. Conclusion
Chapter 8. Tuning swarm behavior for environmental sensing tasks represented as coverage problems
1. Introduction
2. Preliminaries
3. System design: swarming for coverage tasks
4. Experimental analysis
5. Conclusions and future work
Appendix
Chapter 9. Machine learning applications for developing sustainable construction materials
1. Introduction
2. Prediction
3. Damage segmentation and detection
4. Mixture design
5. Multiobjective optimization
6. Conclusions
Chapter 10. The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment
1. Introduction
2. AI-assisted techniques for PFAS detection and removal
3. Sensors for detection of PFAS
4. Biosensors
5. Disinfection by-products
6. AI-assisted techniques for removal of heavy metal
Chapter 11. Recent progress in biosensors for wastewater monitoring and surveillance
1. Introduction
2. Principles and working of BES as a biosensor
3. Biosensor for various pollutant monitoring
4. Photoelectrochemical biosensors
5. Biosensors as a perspective to monitor infectious disease outbreak
6. Conclusions, future trends, and prospective of biosensors
Chapter 12. Machine learning in surface plasmon resonance for environmental monitoring
1. Introduction
2. Surface plasmon resonance
3. Environmental hazard monitoring by SPR
4. Machine learning algorithms in SPR
5. Applications of ML in SPR
6. Conclusion and future perspectives
Index

MA

Mohsen Asadnia

Mohsen Asadnia is an Associate Professor and group lader in Mechatronics-biomechanics and at Macquarie University, Australia. He received his PhD degree in Mechanical Engineering from Nanyang Technological University, Singapore. Prior to joining Macquarie University, Mohsen had several teaching and research roles with the University of Western Australia, Massachusetts Institute of Technology and Nanyang Technological University. His research interest lies in environmental/ biomedical sensors, Artificial Intelligence, and bio-inspired sensing.

Affiliations and expertise

Senior Lecturer, Mechatronics-Biomechanics and an ARC DECRA Fellow, Macquarie University, Australia

AR

Amir Razmjou

Amin Beheshti is a Full Professor of Data Science and the Director of AI-enabled Processes (AIP) Research Centre, School of Computing, Macquarie University. Amin is also the head of the Data Analytics Research Lab and Adjunct Academic in Computer Science at UNSW Sydney. Amin completed his Ph.D. and Postdoc in Computer Science and Engineering at UNSW Sydney and holds a Master and Bachelor in Computer Science both with First Class Honours. He is the leading author of several authored books in data, social, and process analytics, coauthored with other high-profile researchers.

Affiliations and expertise

Senior Research Associate, University of New South Wales (UNSW), Australia

AB

Amin Beheshti

Amin Beheshti is a Full Professor of Data Science and the Director of AI-enabled Processes (AIP) Research Centre, School of Computing, Macquarie University. Amin is also the head of the Data Analytics Research Lab and Adjunct Academic in Computer Science at UNSW Sydney. Amin completed his Ph.D. and Postdoc in Computer Science and Engineering at UNSW Sydney and holds a Master and Bachelor in Computer Science both with First Class Honours. He is the leading author of several authored books in data, social, and process analytics, co-authored with other high-profile researchers.

Affiliations and expertise

Director of AI-enabled Processes (AIP) Research Centre and the head of the Data Analytics Research Lab, Department of Computing, Macquarie University, Sydney, Australia

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Mohsen Asadnia

Amir Razmjou

Amin Beheshti