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E-raamat: Artificial Intelligence and Data Science in Environmental Sensing

Edited by (Professor, Mechatronics-Biomechanics and an ARC DECRA Fellow, Macquarie University, Australia), Edited by (Direct), Edited by (Amir Razmjou is an Associate Professor at Edith Cowan University and the Leader of the Mineral Recovery Research Centre (MRRC).)
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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.
  • Presents tools, connections and proactive solutions to take sustainability programs to the next level
  • Offers a practical guide for making students proficient in modern electronic data analysis and graphics
  • Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery
Contributors xi
Editor Bio xv
Preface xvii
1 Smart sensing technologies for wastewater treatment plants
Reza Maleki
Ahmad Miri Jahromi
Ebrahim Ghasemy
Mohammad Khedri
1 Introduction
1(2)
2 Online estimation
3(2)
3 Fault detection and diagnostics
5(3)
3.1 Electrochemical sensors
7(1)
3.2 Fiber optic sensors for direct monitoring of water quality
7(1)
3.3 Sensors based on microwave technology
8(1)
4 Multivariate analysis models
8(4)
5 Conclusion and future direction
12(7)
References
13(6)
2 Advancements and artificial intelligence approaches in antennas for environmental sensing
Ali Lalbakhsh
Roy B.V.B. Simorangkir
Nima Bayat-Makou
Ahmed A. Kishk
Karu P. Esselle
1 Printed antennas for wireless sensor networks
19(4)
2 Printed antenna sensors for material characterization
23(2)
3 Epidermal antenna for unobtrusive human-centric wireless communications and sensing
25(5)
3.1 Epidermal electronics
25(1)
3.2 Epidermal antennas
26(4)
4 Artificial intelligence in antenna design
30(9)
4.1 Particle swarm optimization in antenna design
31(2)
4.2 Artificial neural network in antenna design
33(1)
References
33(6)
3 Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport
Omid Ghaffarpasand
Ahmad Miri Jahromi
Reza Maleki
Elika Karbassiyazdi
Rhiannon Blake
1 Introduction
39(2)
2 The role of transport in the economy and environment
41(3)
3 Geo-sensing; evolution in the geography
44(3)
4 Geographic Information System as a revolution or/and an evolution
47(2)
5 Geo-sensing for moving toward eco-routing and low-emission transport
49(1)
6 Intelligent geo-sensing and AI as a new window to the future
50(1)
7 Conclusion
51(6)
References
52(5)
4 Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization
A. Yagmur Goren
Yajar K. Recepoglu
Alireza Khataee
1 Introduction
57(1)
2 Strategy of response surface methodology
58(2)
3 Practical application of RSM in electrochemical processes for wastewater treatment
60(22)
3.1 Electrocoagulation
60(9)
3.2 Electro-Fenton
69(8)
3.3 Electro-oxidation
77(4)
3.4 Hybrid processes
81(1)
4 Merits and demerits of RSM
82(1)
5 Conclusions
83(10)
References
83(10)
5 Artificial intelligence and sustainability: solutions to social and environmental challenges
Firouzeh Taghikhah
Eila Erfani
Ivan Bakhshayeshi
Sara Tayari
Alexandros Karatopouzis
Bavly Hanna
1 Introduction
93(2)
2 AI and social change: the case of food and garden waste management
95(4)
2.1 AI-powered analysis of FOGO survey data
96(2)
2.2 Using AI insights to improve waste management
98(1)
3 AI and ecosystem services: insights into bushfire management and renewable energy production
99(4)
3.1 AI role in predicting bushfire occurrence and spread
99(1)
3.2 Artificial intelligence for energy conservation and renewable energy
100(3)
4 Challenges of using AI to achieve sustainability
103(1)
5 Implications and conclusion
103(6)
References
105(4)
6 Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines
Mohammad Yazdi
Arman Nedjati
Esmaeil Zarei
Rouzbeh Abbassi
1 Decision-making in renewable energy investments
109(2)
2 Decision-making tools on the development and design of offshore wind power farms
111(2)
3 Background of multiattribute decision-making tools
113(4)
3.1 VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje)
113(1)
3.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation)
114(2)
3.3 ELECTRE (ELimination Et Choice Translating REality)
116(1)
4 Background of multiobjective problems in offshore and wind farms
117(12)
4.1 Practical studies
117(2)
4.2 Objectives and solution methods
119(1)
4.3 Future directions
119(2)
References
121(8)
7 Recent advances of image processing techniques in agriculture
Helia Farhood
Ivan Bakhshayeshi
Matineh Pooshideh
Nabi Rezvani
Amin Beheshti
1 Introduction
129(1)
2 Application in plants detection
130(7)
2.1 Plant segmentation and extraction in the field
130(2)
2.2 Plant diseases recognition
132(3)
2.3 Three-dimensional monitoring for plant growth
135(2)
3 Application in livestock recognition
137(4)
3.1 Livestock detection
137(1)
3.2 Cattle recognition
138(3)
4 Application in fruits and vegetables recognition
141(6)
4.1 Fruits and vegetables identification and classification
141(2)
4.2 Fruits and vegetables grading and sorting
143(3)
4.3 Fruits and vegetables disease and defect detection
146(1)
5 Conclusion
147(8)
References
149(6)
8 Tuning swarm behavior for environmental sensing tasks represented as coverage problems
Shadi Abpeikar
Kathryn Kasmarik
Phi Vu Tran
Matthew Garratt
Sreenatha Anavatti
Md Mohiuddin Khan
1 Introduction
155(1)
2 Preliminaries
156(5)
2.1 Related work
157(2)
2.2 Reynolds' boid model
159(1)
2.3 Reinforcement learning
159(1)
2.4 Coverage problems
160(1)
3 System design: swarming for coverage tasks
161(4)
3.1 Autonomous tuning of swarm behavior by the reinforcement learning subsystem
161(3)
3.2 Coverage algorithm subsystem
164(1)
4 Experimental analysis
165(8)
4.1 Experiment 1: learning to tune a swarm
165(1)
4.2 Experiment 2: using a tuned swarm to solve a coverage problem
166(5)
4.3 Evaluating the tuning and coverage ability of RL-SBAT on unseen random boids
171(1)
4.4 Evaluating the tuning and coverage ability of RL-SBAT on unseen random movement of robots
172(1)
5 Conclusions and future work
173(6)
Appendix
174(2)
References
176(3)
9 Machine learning applications for developing sustainable construction materials
Hossein Adel
Majid Ilchi Ghazaan
Asghar Habibnejad Korayem
1 Introduction
179(2)
2 Prediction
181(16)
2.1 Fresh properties
181(3)
2.2 Mechanical properties
184(9)
2.3 Durability
193(4)
3 Damage segmentation and detection
197(2)
4 Mixture design
199(2)
5 Multiobjective optimization
201(3)
6 Conclusions
204(7)
References
205(6)
10 The AI-assisted removal and sensor-based detection of contaminants in the aquatic environment
Sweta Modak
Hadi Mokarizadeh
Elika Karbassiyazdi
Ahmad Hosseinzadeh
Milad Rabbabni Esfahani
1 Introduction
211(2)
2 AI-assisted techniques for PFAS detection and removal
213(2)
3 Sensors for detection of PFAS
215(4)
3.1 Electrochemical sensors
215(2)
3.2 Optical and fluorescence sensors
217(2)
4 Biosensors
219(1)
5 Disinfection by-products
220(4)
5.1 AI-assisted techniques for disinfection by-products removal
221(2)
5.2 Sensors for detection of DBPs
223(1)
5.3 Heavy metals
224(1)
6 AI-assisted techniques for removal of heavy metal
224(21)
6.1 Sensors for detection of heavy metals
227(2)
6.2 Antibiotics, endocrine-disrupting chemicals/pharmaceuticals
229(2)
6.3 Sensors for detection of heavy metals antibiotics, endocrine-disrupting chemicals/pharmaceuticals
231(1)
References
232(13)
11 Recent progress in biosensors for wastewater monitoring and surveillance
Pratiksha Srivastava
Yamini Mittal
Supriya Gupta
Rouzbeh Abbassi
Vikram Garaniya
1 Introduction
245(2)
2 Principles and working of BES as a biosensor
247(5)
2.1 Microbial fuel cell as a sensor
247(3)
2.2 Microbial electrolysis cell as a sensor
250(2)
3 Biosensor for various pollutant monitoring
252(3)
3.1 Organic pollutants
252(1)
3.2 Nitrogen pollutants
253(1)
3.3 Toxic pollutants
254(1)
4 Photoelectrochemical biosensors
255(3)
4.1 Photoelectrochemical enzymatic biosensors
257(1)
5 Biosensors as a perspective to monitor infectious disease outbreak
258(3)
6 Conclusions, future trends, and prospective of biosensors
261(8)
References
262(7)
12 Machine learning in surface plasmon resonance for environmental monitoring
Masoud Mohseni-Dargah
Zahra Falahati
Bahareh Dabirmanesh
Parisa Nasrollahi
Khosro Khajeh
1 Introduction
269(1)
2 Surface plasmon resonance
270(3)
2.1 Sensorgram
272(1)
2.2 Other types of SPR platforms
272(1)
3 Environmental hazard monitoring by SPR
273(5)
3.1 Detection of pesticides
273(1)
3.2 Detection of phenolic compounds
274(1)
3.3 Detection of heavy metal ions
274(2)
3.4 Detection of pathogen microorganisms
276(2)
4 Machine learning algorithms in SPR
278(5)
4.1 Supervised machine learning
281(1)
4.2 Unsupervised machine learning
282(1)
5 Applications of ML in SPR
283(6)
6 Conclusion and future perspectives
289(10)
References
290(9)
Index 299
Mohsen Asadnia is a 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. Amir Razmjou is an Associate Professor at Edith Cowan University and the Leader of the Mineral Recovery Research Centre (MRRC).

Associate Professor Amir Razmjou (PhD from the University of New South Wales (UNSW), Sydney, Australia, 2012) is an experienced academic and industry professional with over 20 years of expertise in desalination, water treatment, membrane technology, and mineral processing. As a Board Director of the Membrane Society of Australasia (MSA) and Founder of the Mineral Recovery Research Centre (MRRC) at Edith Cowan

University (ECU), Western Australia, Associate Professor Razmjou has made significant contributions to the fields of mining and resource extraction, particularly in lithium processing.

He has published over 200 peer-reviewed articles and secured research funding



exceeding $9.2 million AUD. Dr. Razmjou has received awards such as the 2024 WA FHRI

Fund Innovation Fellow, the 2023 MSA Industry Innovation Award, and the 2021 UTS Chancellor Research Fellow. He has supervised more than 40 masters and Ph.D. candidates and serves in editorial roles for journals such as Desalination, DWT, and JWPE. At MRRC, he has established a DLE line, including various processes such as membranes, ion exchange, and adsorption at laboratory and pilot scales. His research also includes developing and implementing advanced technologies for DLEs pretreatment and posttreatment to enhance the Li/TDS ratio and purify the final product to battery-grade

quality"

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.
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