Environmental Monitoring Using Artificial Intelligence
1st Edition
Published on 21. March 2025
Book
Hardback
432 pages
978-1-394-27036-1 (ISBN)
Description
Environmental Monitoring Using Artificial Intelligence is a vital resource for anyone looking to leverage cutting-edge technologies in artificial intelligence and sensor systems to effectively address environmental challenges, offering innovative solutions and insights essential for creating a sustainable future.
Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability.
From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you're exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.
Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability.
From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you're exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Dimensions
Height: 158 mm
Width: 237 mm
Thickness: 30 mm
Weight
740 gr
ISBN-13
978-1-394-27036-1 (9781394270361)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
A. Suresh | T. Devi | N. Deepa
Environmental Monitoring Using Artificial Intelligence
E-Book
02/2025
1st Edition
Wiley
€197.99
Available for download
A. Suresh | T. Devi | N. Deepa
Environmental Monitoring Using Artificial Intelligence
E-Book
02/2025
1st Edition
Wiley
€188.99
Available for download
Persons
A. Suresh, PhD, is an associate professor in the Department of Networking and Communications in the School of Computing, at the SRM Institute of Science and Technology, Tamil Nadu, India with over decades of experience. He has been granted nine patents, published over 150 papers in technical journals and over 100 papers at international scientific conferences, as well as several books. Additionally, he has served as an editor and reviewer for various journals and a chair of several conferences and workshops.
T. Devi, PhD, is a professor in the Department of Computer Science and Engineering at the Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. She has published her research work in reputed international journals and presented papers in several national and international and conferences and has published patents in the computer science and engineering field. Her areas of specialization include cryptography, network security, cloud computing, artificial intelligence, machine learning, and blockchain.
N. Deepa, PhD, is an associate professor in the Department of Computer Science and Engineering at the Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. She has presented papers in several national and international conferences, as well as published her research work in reputed international journals and conferences and patents in the computer science and engineering field. Her areas of specialization include deep learning, machine learning, mobile computing, cryptography, network security, and blockchain.
Ali Kashif Bashir, PhD, is a professor at the School of Computing and Mathematics, Manchester Metropolitan University, United Kingdom and an adjunct professor at the School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad. Additionally, he has authored over 90 peer-reviewed articles and advised several startups in the field of STEM-based education, robotics, and smart homes. He is a senior member of the Institute of Electrical and Electronics Engineers and a Distinguished Speaker for the Association for Computing Machinery and serves as an editor and reviewer for a number of journals.
T. Devi, PhD, is a professor in the Department of Computer Science and Engineering at the Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. She has published her research work in reputed international journals and presented papers in several national and international and conferences and has published patents in the computer science and engineering field. Her areas of specialization include cryptography, network security, cloud computing, artificial intelligence, machine learning, and blockchain.
N. Deepa, PhD, is an associate professor in the Department of Computer Science and Engineering at the Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. She has presented papers in several national and international conferences, as well as published her research work in reputed international journals and conferences and patents in the computer science and engineering field. Her areas of specialization include deep learning, machine learning, mobile computing, cryptography, network security, and blockchain.
Ali Kashif Bashir, PhD, is a professor at the School of Computing and Mathematics, Manchester Metropolitan University, United Kingdom and an adjunct professor at the School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad. Additionally, he has authored over 90 peer-reviewed articles and advised several startups in the field of STEM-based education, robotics, and smart homes. He is a senior member of the Institute of Electrical and Electronics Engineers and a Distinguished Speaker for the Association for Computing Machinery and serves as an editor and reviewer for a number of journals.
Content
Preface xv
1 Transformative Trends in AI for Environmental Monitoring: Challenges, Applications 1
Leena Sri R., Divya Vetriveeran, Rakoth Kandan Sambandam, Jenefa J. and Karthikeyan Thangavel
1.1 Introduction 2
1.2 Literature Verticals 3
1.3 Key Methodologies in Literature Review 5
1.4 Most Common Methods in Environmental Monitoring 8
1.5 AI Architectures for Environmental Monitoring 8
1.6 Applications of AI in Environmental Monitoring 17
1.7 Challenges and Limitations of Using AI in Environment Modeling 20
1.8 Future Directions 22
1.9 Conclusion 24
Acknowledgements 25
References 25
2 Fundamentals of AI and NLP in Environmental Analysis 29
Sreedevi Chikkudu and Suresh Annamalai
2.1 Introduction 30
2.2 AI and NLP Techniques 33
2.3 AI Models and NLP System with Data Science Cycle 35
2.4 Environmental Analysis Using AIoT and NLP 39
Bibliography 42
3 Smart Environmental Monitoring Systems: IoT and Sensor-Based Advancements 45
D. Roja Ramani, B. Ben Sujitha and Shrikant Tangade
3.1 Introduction 46
3.2 Essential Elements and Factors for Environmental Monitoring with IoT 49
3.3 Diverse Avenues and Methodologies in IoT Environmental Applications 53
3.4 Conclusion 58
References 58
4 Remote Monitoring Advancements: A New Approach to Biodiversity Conservation 61
D. Roja Ramani, K. Kalaiarasan and Shrikant Tangade
4.1 Introduction 62
4.2 Indicators of Primary Biodiversity 63
4.3 Exploring Biodiversity Conservation Strategies 64
4.4 AI Enhancing Animal Observation Images 65
4.5 AI and ML for Preserving Flora 65
4.6 Deep Learning Tracks Terrestrial Mammals via Satellites 67
4.7 Conclusion 69
References 69
5 Smart Water Solutions: A Case Study on Drone-Led Hydrological Investigation of Water Diversion from Lakshmiyapuram Catchment to Sivakasi Periyakulam Tank 71
I. Baskar, A. Haamidh, S. Suriya and K. Parameswari
5.1 Introduction 72
5.2 Software Used 75
5.3 Methodology 78
5.4 Conclusion and Recommendation 99
Acknowledgement 100
References 100
6 Sustainable Waste Management as a Key Feature for Smart City: A Case Study of Vadodara, Gujarat, India 103
Sahil Menghani, Hardik Giri Gosai, Parashuram Kallem, Payal Desai and Uma Hapani
6.1 Introduction 104
6.2 Material and Methodology 112
6.3 Result and Discussion 120
6.4 Limitation of Study 127
6.5 Conclusion and Future Prospects 128
References 128
7 Sensor Technologies for Environmental Data Collection 133
Adimulam Raghuvira Pratap and Suresh Annamalai
7.1 Introduction 134
7.2 Sensor Technologies 134
7.3 Background of Sensing 135
7.4 Types of Sensors 136
7.5 Applications of Sensors 145
7.6 Challenges of Sensors 148
7.7 Environmental Sensors 149
7.8 Summary and Recommendations 163
Bibliography 164
8 Significance and Advancement of Sensor Technologies for Environmental Analysis 167
S. Thanga Revathi, Mary Subaja Christo, A. Sathya and Suresh Annamalai
8.1 Introduction 168
8.2 Sensing and Sensor Fundamentals 169
8.3 Key Sensor Technology Components 174
8.4 Regulations and Standards - Sensor Technologies 178
8.5 Conclusion 179
Bibliography 179
9 Texture-Based Classification of Organic and Pesticidal Spinach Using Machine Learning 181
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Ganesha Ram G., Ashween Raj V. S. and Godwin Wilfred Raj A.
9.1 Introduction 182
9.2 Related Works 183
9.3 Proposed Work 186
9.4 Implementation and Results 193
9.5 Conclusion 197
References 198
10 Deep Bidirectional LSTM for Emotion Detection through Mobile Sensor Analysis 201
D. Roja Ramani, Naveen Chandra Gowda, S. Sreejith and Shrikant Tangade
10.1 Introduction 202
10.2 Literature Survey 206
10.3 Methodology 209
10.4 Results and Discussion 215
10.5 Conclusion 218
10.6 Future Directions 219
References 220
11 A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection 225
Jenefa J., Divya Vetriveeran, Rakoth Kandan Sambandam, Vinodha D., S. Thaiyalnayaki and P. Karthikeyan
11.1 Introduction 226
11.2 Related Works 227
11.3 AlexNet 234
11.4 ResNet 237
11.5 Proposed Work 240
11.6 Conclusion 247
Acknowledgments 247
References 247
12 Comparison of Borewell Rescue L-Type Different Arm with Different Materials 251
K.P. Sridhar, Arun M., C. Prajitha, S. Deepa, Abubeker K.M. and Rajalakshmi Selvaraj
12.1 Introduction 252
12.2 Related Works 253
12.3 Proposed Method 255
12.4 Cylinder 255
12.5 Ellipse 259
12.6 I-Beam 262
12.7 L-Angle 265
12.8 Mathematical Analysis 269
12.9 Results and Discussion 271
12.10 Conclusion 275
References 276
13 Optimizing Almond and Walnut Farming: A U-Net-Powered Deep Learning Approach for Energy Efficiency Prediction and Damage Assessment 279
D. Roja Ramani, N. Deepa, Naveen Chandra Gowda and Naandhini Sidnal
13.1 Introduction 280
13.2 Literature Survey 284
13.3 Methodology 290
13.4 Results and Discussion 294
13.5 Conclusion 298
References 299
14 Enhancing Sustainable Management of Waste Dump Sites with Smart Drones and Geospatial Tech: Air Quality Monitoring and Analysis 303
Naveen Chandra Gowda, Veena H. N., Aghila Rajagopal and Shrikant Tangade
14.1 Introduction 304
14.2 Review of Relevant Literature 307
14.3 Methodological Framework 310
14.4 Outcomes and Discourse 316
14.5 Conclusion 321
References 321
15 Voltage Veggies: A Shocking Revolution in Agriculture 325
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Rageshwaran H.R., Praveen Kumar D., Praveen Kumar S. and Mugunthan Kennedy K.
15.1 Introduction 326
15.2 Proposed Methodology 330
15.3 Experimental Approach 343
15.4 Conclusion and Future Research Directions 348
15.5 Conclusion 350
References 350
16 Emperor Penguin Optimized Loop Selection Process for Routerless NoC Design 353
N.L. Venkataraman, S. Sumithra, S. Suresh Kumarm, K. Kokulavani and Gunasekaran Thangevel
16.1 Introduction 354
16.2 Related Works 355
16.3 Design of Routerless NoC 356
16.4 Emperor Penguin Optimized (EPO) Loop Selection 358
16.5 Result and Discussion 364
16.6 Conclusion 371
References 372
17 Case Study on Flyover Construction and the Air Quality Measurement by the Emission Level of Pollutants 375
K.P. Sridhar, C. Prajitha, S. Deepa, Rinesh.S, Arun.M and Srinath Doss
17.1 Introduction 376
17.2 Related Study 377
17.3 Case Study on Flyover Construction and the Air Quality Measurement 378
17.4 Conclusion 384
References 385
About the Editors 389
Index 391
1 Transformative Trends in AI for Environmental Monitoring: Challenges, Applications 1
Leena Sri R., Divya Vetriveeran, Rakoth Kandan Sambandam, Jenefa J. and Karthikeyan Thangavel
1.1 Introduction 2
1.2 Literature Verticals 3
1.3 Key Methodologies in Literature Review 5
1.4 Most Common Methods in Environmental Monitoring 8
1.5 AI Architectures for Environmental Monitoring 8
1.6 Applications of AI in Environmental Monitoring 17
1.7 Challenges and Limitations of Using AI in Environment Modeling 20
1.8 Future Directions 22
1.9 Conclusion 24
Acknowledgements 25
References 25
2 Fundamentals of AI and NLP in Environmental Analysis 29
Sreedevi Chikkudu and Suresh Annamalai
2.1 Introduction 30
2.2 AI and NLP Techniques 33
2.3 AI Models and NLP System with Data Science Cycle 35
2.4 Environmental Analysis Using AIoT and NLP 39
Bibliography 42
3 Smart Environmental Monitoring Systems: IoT and Sensor-Based Advancements 45
D. Roja Ramani, B. Ben Sujitha and Shrikant Tangade
3.1 Introduction 46
3.2 Essential Elements and Factors for Environmental Monitoring with IoT 49
3.3 Diverse Avenues and Methodologies in IoT Environmental Applications 53
3.4 Conclusion 58
References 58
4 Remote Monitoring Advancements: A New Approach to Biodiversity Conservation 61
D. Roja Ramani, K. Kalaiarasan and Shrikant Tangade
4.1 Introduction 62
4.2 Indicators of Primary Biodiversity 63
4.3 Exploring Biodiversity Conservation Strategies 64
4.4 AI Enhancing Animal Observation Images 65
4.5 AI and ML for Preserving Flora 65
4.6 Deep Learning Tracks Terrestrial Mammals via Satellites 67
4.7 Conclusion 69
References 69
5 Smart Water Solutions: A Case Study on Drone-Led Hydrological Investigation of Water Diversion from Lakshmiyapuram Catchment to Sivakasi Periyakulam Tank 71
I. Baskar, A. Haamidh, S. Suriya and K. Parameswari
5.1 Introduction 72
5.2 Software Used 75
5.3 Methodology 78
5.4 Conclusion and Recommendation 99
Acknowledgement 100
References 100
6 Sustainable Waste Management as a Key Feature for Smart City: A Case Study of Vadodara, Gujarat, India 103
Sahil Menghani, Hardik Giri Gosai, Parashuram Kallem, Payal Desai and Uma Hapani
6.1 Introduction 104
6.2 Material and Methodology 112
6.3 Result and Discussion 120
6.4 Limitation of Study 127
6.5 Conclusion and Future Prospects 128
References 128
7 Sensor Technologies for Environmental Data Collection 133
Adimulam Raghuvira Pratap and Suresh Annamalai
7.1 Introduction 134
7.2 Sensor Technologies 134
7.3 Background of Sensing 135
7.4 Types of Sensors 136
7.5 Applications of Sensors 145
7.6 Challenges of Sensors 148
7.7 Environmental Sensors 149
7.8 Summary and Recommendations 163
Bibliography 164
8 Significance and Advancement of Sensor Technologies for Environmental Analysis 167
S. Thanga Revathi, Mary Subaja Christo, A. Sathya and Suresh Annamalai
8.1 Introduction 168
8.2 Sensing and Sensor Fundamentals 169
8.3 Key Sensor Technology Components 174
8.4 Regulations and Standards - Sensor Technologies 178
8.5 Conclusion 179
Bibliography 179
9 Texture-Based Classification of Organic and Pesticidal Spinach Using Machine Learning 181
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Ganesha Ram G., Ashween Raj V. S. and Godwin Wilfred Raj A.
9.1 Introduction 182
9.2 Related Works 183
9.3 Proposed Work 186
9.4 Implementation and Results 193
9.5 Conclusion 197
References 198
10 Deep Bidirectional LSTM for Emotion Detection through Mobile Sensor Analysis 201
D. Roja Ramani, Naveen Chandra Gowda, S. Sreejith and Shrikant Tangade
10.1 Introduction 202
10.2 Literature Survey 206
10.3 Methodology 209
10.4 Results and Discussion 215
10.5 Conclusion 218
10.6 Future Directions 219
References 220
11 A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection 225
Jenefa J., Divya Vetriveeran, Rakoth Kandan Sambandam, Vinodha D., S. Thaiyalnayaki and P. Karthikeyan
11.1 Introduction 226
11.2 Related Works 227
11.3 AlexNet 234
11.4 ResNet 237
11.5 Proposed Work 240
11.6 Conclusion 247
Acknowledgments 247
References 247
12 Comparison of Borewell Rescue L-Type Different Arm with Different Materials 251
K.P. Sridhar, Arun M., C. Prajitha, S. Deepa, Abubeker K.M. and Rajalakshmi Selvaraj
12.1 Introduction 252
12.2 Related Works 253
12.3 Proposed Method 255
12.4 Cylinder 255
12.5 Ellipse 259
12.6 I-Beam 262
12.7 L-Angle 265
12.8 Mathematical Analysis 269
12.9 Results and Discussion 271
12.10 Conclusion 275
References 276
13 Optimizing Almond and Walnut Farming: A U-Net-Powered Deep Learning Approach for Energy Efficiency Prediction and Damage Assessment 279
D. Roja Ramani, N. Deepa, Naveen Chandra Gowda and Naandhini Sidnal
13.1 Introduction 280
13.2 Literature Survey 284
13.3 Methodology 290
13.4 Results and Discussion 294
13.5 Conclusion 298
References 299
14 Enhancing Sustainable Management of Waste Dump Sites with Smart Drones and Geospatial Tech: Air Quality Monitoring and Analysis 303
Naveen Chandra Gowda, Veena H. N., Aghila Rajagopal and Shrikant Tangade
14.1 Introduction 304
14.2 Review of Relevant Literature 307
14.3 Methodological Framework 310
14.4 Outcomes and Discourse 316
14.5 Conclusion 321
References 321
15 Voltage Veggies: A Shocking Revolution in Agriculture 325
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Rageshwaran H.R., Praveen Kumar D., Praveen Kumar S. and Mugunthan Kennedy K.
15.1 Introduction 326
15.2 Proposed Methodology 330
15.3 Experimental Approach 343
15.4 Conclusion and Future Research Directions 348
15.5 Conclusion 350
References 350
16 Emperor Penguin Optimized Loop Selection Process for Routerless NoC Design 353
N.L. Venkataraman, S. Sumithra, S. Suresh Kumarm, K. Kokulavani and Gunasekaran Thangevel
16.1 Introduction 354
16.2 Related Works 355
16.3 Design of Routerless NoC 356
16.4 Emperor Penguin Optimized (EPO) Loop Selection 358
16.5 Result and Discussion 364
16.6 Conclusion 371
References 372
17 Case Study on Flyover Construction and the Air Quality Measurement by the Emission Level of Pollutants 375
K.P. Sridhar, C. Prajitha, S. Deepa, Rinesh.S, Arun.M and Srinath Doss
17.1 Introduction 376
17.2 Related Study 377
17.3 Case Study on Flyover Construction and the Air Quality Measurement 378
17.4 Conclusion 384
References 385
About the Editors 389
Index 391