Artificial intelligence and data science in environmental sensing:
Saved in:
| Other Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London
Elsevier
[2022]
London Academic Press |
| Series: | Cognitive data science in sustainable computing
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xviii, 305 Seiten Illustrationen, Diagramme |
| ISBN: | 9780323905084 |
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Contents Contributors Editor Bio Preface xi xv xvii treatment plants Reza Maleki, Ahmad Miri Jahromi, Ebrahim Ghasemy and Mohammad Khedri 1 3 5 1. 2. 3. Introduction Online estimation Fault detection and diagnostics 3.1 Electrochemical sensors 3.2 Fiber optic sensors for direct monitoring of water quality 3.3 Sensors based on microwave technology 4. Multivariate analysis models 5. Conclusion and future direction References 7 7 8 8 12 13 Advancements and artificial intelligence approaches in antennas for environmental sensing AH Lalbakhsh, Roy B.V.B. Simorangkir, Nima Bayat-Makou, Ahmed A. Kishk and Karu P. Esselle Printed antennas for wireless sensor networks 19 Printed antenna sensors for material characterization 23 3. Epidermal antenna for unobtrusive human-centric wireless communications and sensing 25 3.1 Epidermal electronics 25 3.2 Epidermal antennas 26 4. Artificial intelligence in antenna design 30 4.1 Particle swarm optimization inantennadesign 31 4.2 Artificial neural network inantenna design 33 References 33 1. 2. v
vi 3. Contents Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport Omid Ghaffarpasand, Ahmad Miri Jahromi, Reza Maleki, Elika Karbassiyazdi and Rhiannon Blake Introduction The role of transport in the economy and environment Geo-sensing; evolution in the geography Geographic Information System as a revolution օր/and an evolution 5. Geo-sensing for moving toward eco-routing and low-emission transport 6. Intelligent geo-sensing and Al as a new window to the future 7. Conclusion References 1. 2. 3. 4. 4. 39 41 44 47 49 50 51 52 Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization A. Yagmur Goren, Yaşar K. Recepoğlu and Alireza Khataee Introduction Strategy of response surface methodology Practical application of RSM in electrochemical processes for wastewater treatment 3.1 Electrocoagulation 3.2 Electro-Fenton 3.3 Electro-oxidation 3.4 Hybrid processes 4. Merits and demerits of RSM 5. Conclusions References 1. 2. 3. 5. 57 58 60 60 69 77 81 82 83 83 Artificial intelligence and sustainability: solutions to social and environmental challenges Firouzeh Taghikhah, Eila Erfani, Ivan Bakhshayeshi, Sara Tayari, Alexandros Karatopouzis and Bavly Hanna 1. 2. 3. Introduction Al and social change: the case of food and garden waste management 2.1 Al-powered analysis of FOGO survey data 2.2 Using Al insights to improve waste management Al and ecosystem services: insights into bushfire management and renewable energy production 3.1 Al role in predicting bushfire
occurrence and spread 93 95 96 98 99 99
Contents vii 3.2 Artificial intelligence for energy conservation and renewable energy 4. Challenges of using Al toachieve sustainability 5. Implications and conclusion References 6. 100 103 103 105 Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines Mohammad Yazdi, Arman Nedjati, Esmaeil Zarei and Rouzbeh Abbassi Decision-making in renewable energy investments 109 Decision-making tools on the development and design of offshore wind power farms 111 3. Background of multiattribute decision-making tools 113 3.1 VIKOR (VIseKriterijumska Optimizacija I Kompromisno Rese nj e) 113 3.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) 114 3.3 ELECTRE (ELimination Et Choice Translating REality) 116 4. Background of multiobjective problems in offshore and wind farms 117 4.1 Practical studies 117 4.2 Objectives and solution methods 119 4.3 Future directions 119 References 121 1. 2. 7. Recent advances of image processing techniques in agriculture Helia Farhood, Ivan Bakhshayeshi, Matineh Pooshideh, Nabi Rezvani and Amin Beheshti Introduction Application in plants detection 2.1 Plant segmentation and extraction in thefield 2.2 Plant diseases recognition 2.3 Three-dimensional monitoring for plant growth 3. Application in livestock recognition 3.1 Livestock detection 3.2 Cattle recognition 4. Application in fruits and vegetablesrecognition 4.1 Fruits and vegetables identification and classification 4.2 Fruits and vegetables grading and sorting 4.3 Fruits and vegetables disease and defect detection 5. Conclusion
References 1. 2. 129 130 130 132 135 137 137 138 141 141 143 146 147 149
viii 8. Contents Tuning swarm behavior for environmental sensing tasks represented as coverage problems Shadi Abpeikar, Kathryn Kasmarik, Phi Vu Tran, Matthew Carratt, Sreenatha Anavatti and Md Mohiuddin Khan Introduction Preliminaries 2.1 Related work 2.2 Reynolds' boid model 2.3 Reinforcement learning 2.4 Coverage problems 3. System design: swarming for coverage tasks 3.1 Autonomous tuning of swarmbehavior by the reinforcement learning subsystem 3.2 Coverage algorithm subsystem 4. Experimental analysis 4.1 Experiment 1 : learning totune a swarm 4.2 Experiment 2: using a tuned swarm to solve a coverage problem 4.3 Evaluating the tuning and coverage ability of RL-SBAT on unseen random boids 4.4 Evaluating the tuning and coverage ability of RL-SBAT on unseen random movement of robots 5. Conclusions and future work Appendix References 1. 2. 9. 155 156 157 159 159 160 161 161 164 165 165 166 1 71 1 72 1 73 174 176 Machine learning applications for developing sustainable construction materials Hossein Adel, Majid Uchi Chazaan and Asghar Habibnejad Korayem 1. Introduction 2. Prediction 2.1 Fresh properties 2.2 Mechanical properties 2.3 Durability 3. Damage segmentation and detection 4. Mixture design 5. Multiobjective optimization 6. Conclusions References 179 181 181 184 193 197 199 201 204 205
Contents 10. ¡X The Al֊assisted removal and sensor-based detection of contaminants in the aquatic environment Sweta Modak, Hadi Mokarizadeh, Elika Karbassiyazdi, Ahmad Hosseinzadeh and Milad Rabbabni Esfahani Introduction Al-assisted techniques for PFAS detection and removal Sensors for detection of PFAS 3.1 Electrochemical sensors 3.2 Optical and fluorescence sensors 4. Biosensors 5. Disinfection by-products 5.1 Al-assisted techniques for disinfection by-products removal 5.2 Sensors for detection of DBPs 5.3 Heavy metals 6. Al-assisted techniques for removal of heavy metal 6.1 Sensors for detection of heavy metals 6.2 Antibiotics, endocrine-disrupting chemicals/ pharmaceuticals 6.3 Sensors for detection of heavy metals antibiotics, endocrine-disrupting chemicals/pharmaceuticals References 1. 2. 3. 11. 211 213 215 215 217 219 220 221 223 224 224 227 229 231 232 Recent progress in biosensors for wastewater monitoring and surveillance Pratiksha Srivastava, Yamini Mittal, Supriya Gupta, Rouzbeh Abbassi and Vikram Garaniya Introduction Principles and working of BES as abiosensor 2.1 Microbial fuel cell as a sensor 2.2 Microbial electrolysis cell as asensor 3. Biosensor for various pollutant monitoring 3.1 Organic pollutants 3.2 Nitrogen pollutants 3.3 Toxic pollutants 4. Photoelectrochemical biosensors 4.1 Photoelectrochemical enzymaticbiosensors 5. Biosensors as a perspective to monitor infectious disease outbreak 6. Conclusions, future trends, and prospective of biosensors References 1. 2. 245 247 247 250 252 252 253 254 255 257 258 261 262
x Contents 12. Machine learning in surface plasmon resonance for environmental monitoring Masoud Mohseni-Dargah, Zahra Falahati, Bahareh Dabirmanesh, Parisa Nasrollahi and Khosro Khajeh 1. Introduction 2. Surface plasmon resonance 2.1 Sensorgram 2.2 Other types of SPR platforms 3. Environmental hazard monitoring by SPR 3.1 Detection of pesticides 3.2 Detection of phenolic compounds 3.3 Detection of heavy metal ions 3.4 Detection of pathogen microorganisms 4. Machine learning algorithms in SPR 4.1 Supervised machine learning 4.2 Unsupervised machine learning 5. Applications of ML in SPR 6. Conclusion and future perspectives References Index 269 270 272 272 273 273 274 274 276 278 281 282 283 289 290 299 |
| adam_txt |
Contents Contributors Editor Bio Preface xi xv xvii treatment plants Reza Maleki, Ahmad Miri Jahromi, Ebrahim Ghasemy and Mohammad Khedri 1 3 5 1. 2. 3. Introduction Online estimation Fault detection and diagnostics 3.1 Electrochemical sensors 3.2 Fiber optic sensors for direct monitoring of water quality 3.3 Sensors based on microwave technology 4. Multivariate analysis models 5. Conclusion and future direction References 7 7 8 8 12 13 Advancements and artificial intelligence approaches in antennas for environmental sensing AH Lalbakhsh, Roy B.V.B. Simorangkir, Nima Bayat-Makou, Ahmed A. Kishk and Karu P. Esselle Printed antennas for wireless sensor networks 19 Printed antenna sensors for material characterization 23 3. Epidermal antenna for unobtrusive human-centric wireless communications and sensing 25 3.1 Epidermal electronics 25 3.2 Epidermal antennas 26 4. Artificial intelligence in antenna design 30 4.1 Particle swarm optimization inantennadesign 31 4.2 Artificial neural network inantenna design 33 References 33 1. 2. v
vi 3. Contents Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport Omid Ghaffarpasand, Ahmad Miri Jahromi, Reza Maleki, Elika Karbassiyazdi and Rhiannon Blake Introduction The role of transport in the economy and environment Geo-sensing; evolution in the geography Geographic Information System as a revolution օր/and an evolution 5. Geo-sensing for moving toward eco-routing and low-emission transport 6. Intelligent geo-sensing and Al as a new window to the future 7. Conclusion References 1. 2. 3. 4. 4. 39 41 44 47 49 50 51 52 Language of response surface methodology as an experimental strategy for electrochemical wastewater treatment process optimization A. Yagmur Goren, Yaşar K. Recepoğlu and Alireza Khataee Introduction Strategy of response surface methodology Practical application of RSM in electrochemical processes for wastewater treatment 3.1 Electrocoagulation 3.2 Electro-Fenton 3.3 Electro-oxidation 3.4 Hybrid processes 4. Merits and demerits of RSM 5. Conclusions References 1. 2. 3. 5. 57 58 60 60 69 77 81 82 83 83 Artificial intelligence and sustainability: solutions to social and environmental challenges Firouzeh Taghikhah, Eila Erfani, Ivan Bakhshayeshi, Sara Tayari, Alexandros Karatopouzis and Bavly Hanna 1. 2. 3. Introduction Al and social change: the case of food and garden waste management 2.1 Al-powered analysis of FOGO survey data 2.2 Using Al insights to improve waste management Al and ecosystem services: insights into bushfire management and renewable energy production 3.1 Al role in predicting bushfire
occurrence and spread 93 95 96 98 99 99
Contents vii 3.2 Artificial intelligence for energy conservation and renewable energy 4. Challenges of using Al toachieve sustainability 5. Implications and conclusion References 6. 100 103 103 105 Application of multi-criteria decision-making tools for a site analysis of offshore wind turbines Mohammad Yazdi, Arman Nedjati, Esmaeil Zarei and Rouzbeh Abbassi Decision-making in renewable energy investments 109 Decision-making tools on the development and design of offshore wind power farms 111 3. Background of multiattribute decision-making tools 113 3.1 VIKOR (VIseKriterijumska Optimizacija I Kompromisno Rese nj e) 113 3.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) 114 3.3 ELECTRE (ELimination Et Choice Translating REality) 116 4. Background of multiobjective problems in offshore and wind farms 117 4.1 Practical studies 117 4.2 Objectives and solution methods 119 4.3 Future directions 119 References 121 1. 2. 7. Recent advances of image processing techniques in agriculture Helia Farhood, Ivan Bakhshayeshi, Matineh Pooshideh, Nabi Rezvani and Amin Beheshti Introduction Application in plants detection 2.1 Plant segmentation and extraction in thefield 2.2 Plant diseases recognition 2.3 Three-dimensional monitoring for plant growth 3. Application in livestock recognition 3.1 Livestock detection 3.2 Cattle recognition 4. Application in fruits and vegetablesrecognition 4.1 Fruits and vegetables identification and classification 4.2 Fruits and vegetables grading and sorting 4.3 Fruits and vegetables disease and defect detection 5. Conclusion
References 1. 2. 129 130 130 132 135 137 137 138 141 141 143 146 147 149
viii 8. Contents Tuning swarm behavior for environmental sensing tasks represented as coverage problems Shadi Abpeikar, Kathryn Kasmarik, Phi Vu Tran, Matthew Carratt, Sreenatha Anavatti and Md Mohiuddin Khan Introduction Preliminaries 2.1 Related work 2.2 Reynolds' boid model 2.3 Reinforcement learning 2.4 Coverage problems 3. System design: swarming for coverage tasks 3.1 Autonomous tuning of swarmbehavior by the reinforcement learning subsystem 3.2 Coverage algorithm subsystem 4. Experimental analysis 4.1 Experiment 1 : learning totune a swarm 4.2 Experiment 2: using a tuned swarm to solve a coverage problem 4.3 Evaluating the tuning and coverage ability of RL-SBAT on unseen random boids 4.4 Evaluating the tuning and coverage ability of RL-SBAT on unseen random movement of robots 5. Conclusions and future work Appendix References 1. 2. 9. 155 156 157 159 159 160 161 161 164 165 165 166 1 71 1 72 1 73 174 176 Machine learning applications for developing sustainable construction materials Hossein Adel, Majid Uchi Chazaan and Asghar Habibnejad Korayem 1. Introduction 2. Prediction 2.1 Fresh properties 2.2 Mechanical properties 2.3 Durability 3. Damage segmentation and detection 4. Mixture design 5. Multiobjective optimization 6. Conclusions References 179 181 181 184 193 197 199 201 204 205
Contents 10. ¡X The Al֊assisted removal and sensor-based detection of contaminants in the aquatic environment Sweta Modak, Hadi Mokarizadeh, Elika Karbassiyazdi, Ahmad Hosseinzadeh and Milad Rabbabni Esfahani Introduction Al-assisted techniques for PFAS detection and removal Sensors for detection of PFAS 3.1 Electrochemical sensors 3.2 Optical and fluorescence sensors 4. Biosensors 5. Disinfection by-products 5.1 Al-assisted techniques for disinfection by-products removal 5.2 Sensors for detection of DBPs 5.3 Heavy metals 6. Al-assisted techniques for removal of heavy metal 6.1 Sensors for detection of heavy metals 6.2 Antibiotics, endocrine-disrupting chemicals/ pharmaceuticals 6.3 Sensors for detection of heavy metals antibiotics, endocrine-disrupting chemicals/pharmaceuticals References 1. 2. 3. 11. 211 213 215 215 217 219 220 221 223 224 224 227 229 231 232 Recent progress in biosensors for wastewater monitoring and surveillance Pratiksha Srivastava, Yamini Mittal, Supriya Gupta, Rouzbeh Abbassi and Vikram Garaniya Introduction Principles and working of BES as abiosensor 2.1 Microbial fuel cell as a sensor 2.2 Microbial electrolysis cell as asensor 3. Biosensor for various pollutant monitoring 3.1 Organic pollutants 3.2 Nitrogen pollutants 3.3 Toxic pollutants 4. Photoelectrochemical biosensors 4.1 Photoelectrochemical enzymaticbiosensors 5. Biosensors as a perspective to monitor infectious disease outbreak 6. Conclusions, future trends, and prospective of biosensors References 1. 2. 245 247 247 250 252 252 253 254 255 257 258 261 262
x Contents 12. Machine learning in surface plasmon resonance for environmental monitoring Masoud Mohseni-Dargah, Zahra Falahati, Bahareh Dabirmanesh, Parisa Nasrollahi and Khosro Khajeh 1. Introduction 2. Surface plasmon resonance 2.1 Sensorgram 2.2 Other types of SPR platforms 3. Environmental hazard monitoring by SPR 3.1 Detection of pesticides 3.2 Detection of phenolic compounds 3.3 Detection of heavy metal ions 3.4 Detection of pathogen microorganisms 4. Machine learning algorithms in SPR 4.1 Supervised machine learning 4.2 Unsupervised machine learning 5. Applications of ML in SPR 6. Conclusion and future perspectives References Index 269 270 272 272 273 273 274 274 276 278 281 282 283 289 290 299 |
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| record_format | marc |
| series2 | Cognitive data science in sustainable computing |
| spelling | Artificial intelligence and data science in environmental sensing edited by Mohsen Asadnia, Amir Razmjou, Amin Beheshti London Elsevier [2022] London Academic Press xviii, 305 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Cognitive data science in sustainable computing Umweltüberwachung (DE-588)4278451-7 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Fernerkundung (DE-588)4016796-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Science (DE-588)1140936166 s Künstliche Intelligenz (DE-588)4033447-8 s Datenanalyse (DE-588)4123037-1 s Fernerkundung (DE-588)4016796-3 s Umweltüberwachung (DE-588)4278451-7 s DE-604 Asadnia, Mohsen edt Razmjou, Amir edt Beheshti, Amin (DE-588)1273349369 edt Erscheint auch als Online-Ausgabe 978-0-323-90507-7 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033752823&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Artificial intelligence and data science in environmental sensing Umweltüberwachung (DE-588)4278451-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Fernerkundung (DE-588)4016796-3 gnd |
| subject_GND | (DE-588)4278451-7 (DE-588)4033447-8 (DE-588)4123037-1 (DE-588)1140936166 (DE-588)4016796-3 (DE-588)4143413-4 |
| title | Artificial intelligence and data science in environmental sensing |
| title_auth | Artificial intelligence and data science in environmental sensing |
| title_exact_search | Artificial intelligence and data science in environmental sensing |
| title_exact_search_txtP | Artificial intelligence and data science in environmental sensing |
| title_full | Artificial intelligence and data science in environmental sensing edited by Mohsen Asadnia, Amir Razmjou, Amin Beheshti |
| title_fullStr | Artificial intelligence and data science in environmental sensing edited by Mohsen Asadnia, Amir Razmjou, Amin Beheshti |
| title_full_unstemmed | Artificial intelligence and data science in environmental sensing edited by Mohsen Asadnia, Amir Razmjou, Amin Beheshti |
| title_short | Artificial intelligence and data science in environmental sensing |
| title_sort | artificial intelligence and data science in environmental sensing |
| topic | Umweltüberwachung (DE-588)4278451-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Fernerkundung (DE-588)4016796-3 gnd |
| topic_facet | Umweltüberwachung Künstliche Intelligenz Datenanalyse Data Science Fernerkundung Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033752823&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT asadniamohsen artificialintelligenceanddatascienceinenvironmentalsensing AT razmjouamir artificialintelligenceanddatascienceinenvironmentalsensing AT beheshtiamin artificialintelligenceanddatascienceinenvironmentalsensing |