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Environmental Monitoring Using Artificial Intelligence
A. Suresh (Edited by), Suresh (Author), T. Devi (Edited by), N. Deepa (Edited by), Ali Kashif Bashir (Edited by)
9781394270361, Wiley
Hardback, published 21 March 2025
432 pages
22.9 x 15.2 x 2.6 cm, 0.794 kg
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.
Preface xv 1 Transformative Trends in AI for Environmental Monitoring: Challenges, Applications 1 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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
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Subject Areas: The environment [RN]
