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Microplastic monitoring using artificial intelligence
Revolutionize your approach to environmental protection with this groundbreaking resource, which details how to replace labor-intensive manual analysis with deep learning and explainable AI (XAI) to achieve precise, real-time identification and scalable monitoring of microplastic pollution. AI-driven microplastic monitoring sits at the intersection of environmental science, artificial intelligence, and data analytics, representing a rapidly developing frontier in both research and industry. Microplastic pollution, which has become a critical environmental and public health concern, is challenging to monitor using traditional techniques due to the vast scale, complexity, and minute size of microplastics. Conventional methods, such as manual filtration, microscopic examination, and chemical analysis, are often labor-intensive, time-consuming, and limited in their ability to provide real-time, large-scale data. This book is a groundbreaking exploration of how artificial intelligence, particularly deep learning and explainable AI (XAI), is revolutionizing microplastic research. It highlights innovative applications of deep learning for precise identification and classification of microplastics, while emphasizing the role of XAI in providing transparency and interpretability to AI-driven methods. By integrating these approaches with advanced sensing technologies and predictive models, the book addresses key limitations of traditional methods, offering robust solutions for scalable and accurate monitoring. Additionally, the book considers the ethical, regulatory, and policy implications of deploying AI in environmental science, providing a balanced perspective on the potential benefits and challenges. With contributions from leading researchers and practitioners, this book is an essential resource for environmental scientists, data scientists, policymakers, and technologists committed to sustainable solutions for combating microplastic pollution
Preface xv 1 Introduction to Microplastic and the Role of AI 1Pooja Dixit, Shaloo Dadheech, Priya Batta and Neeraj Bhargava 1.1 Introduction 21.2 Microplastic Distribution and Pathways 51.3 Current Methods of Microplastic Detection 81.4 Role of Artificial Intelligence (AI) in Microplastic Research 121.5 Case Studies and Applications 161.6 Challenges and Limitations 181.7 Future Directions 201.8 Conclusion 21 2 A CNN-ViT Hybrid Deep Learning Architecture for Accurate Microplastic Detection 23B. Dhanalaxmi, B. Saritha, P. Punitha, G. Jagan Naik and B. Anupama 2.1 Introduction 242.2 Literature Review 262.3 Proposed Mythology 292.4 Result and Discussion 312.5 Concluding Remarks and Future Scope 33 3 XAI for Decision Support in Microplastic Pollution Management 37Srinibas Pattanaik, Sachin Ahuja, Sartajvir Singh Dhillon, Jasneet Chawla, Deeksha Sonal and Alessandro Vinciarelli 3.1 Introduction 383.2 Causes and Consequences and Effects of Microplastic Pollution 403.3 The Application of AI in Management of the Environment 423.4 XAI Frameworks are Flexible and for the Micro Plastic Environmental Management and the Summary to Explainable Artificial Intelligence 433.5 Application and Case Studies of XAI Microplastic Pollution Management 453.6 The Utilization of Machine Learning with Explainable AI (XAI) Regarding Decision Support Systems 483.7 Futures Directions and Challenges of Explainable AI with Microplastic Pollution 493.8 Conclusion 51 4 AI-Driven Technologies in Mitigation of Microplastic Pollution 55Lata Rani, Hurmat, Deepa Singh, Babu Bharman, Arun Lal Srivastav, Jyotsna Kaushal, Komal Thapa and Neha Kanojia 4.1 Introduction 564.2 AI Assisted Detection Techniques for the Microplastic 604.3 Application of AI in Microplastic Pollution Control 714.4 Conclusion 74 5 AI Driven Optical Imaging and Spectroscopic Techniques 83Muchukota Sushma, Mekkanti Manasa Rekha, Ramya C. V. and Zaid Khan List of Abbreviations 845.1 Introduction 845.2 Fundamentals of Optical Imaging and Spectroscopic Techniques 905.3 AI Innovations in Microplastic Detection 925.4 Applications in Real-Time Monitoring 945.5 Case Studies in AI-Driven Microplastic Detection 955.6 Challenges in AI-Driven Microplastic Monitoring 975.7 Future Directions 995.8 Conclusion 101 6 Integrating AI with Advanced Sensor Technologies for Real-Time Monitoring 109Avnish Chauhan, Shivam Attri, Aanchal Saklani, Prabhat K. Chauhan, Man Vir Singh, Vishal Rajput, Muneesh Sethi and Samuele Barrili 6.1 Introduction 1106.2 Bibliographic Study 1116.3 AI-Enabled Sensor Technologies for Microplastic Detection 1136.4 Challenges and Future Prospects 1206.5 Conclusion 122 7 Machine Learning for Microplastic Source and Pathway Prediction 127Vanshika and Neetu Rani 7.1 Introduction 1287.2 Microplastic Sources and Pathways: An Overview 1307.3 Data Acquisition and Preprocessing 1327.4 Machine Learning Approaches for Microplastic Modeling 1347.5 Model Development and Validation 1377.6 Case Studies and Real-World Implementations 1387.7 Visualization and Decision Support 1387.8 Challenges and Ethical Considerations 1427.9 Conclusion and Future Scope 143 8 Big Data Analytics in Mapping the Global Microplastic Distribution 147Prasann Kumar 8.1 Introduction 1488.2 Data Sources for Microplastic Mapping 1528.3 Big Data Techniques in Microplastic Analytics 1558.4 Challenges in Big Data for Microplastic Studies 1598.5 Case Studies 1638.6 Applications and Implications 1668.7 Future Directions 1708.8 Conclusion 1738.9 Ackno
edited by Abhishek Kumar, Pooja Dixit, Pramod Singh Rathore, Arun Lal Srivastav, Ashutosh Kumar Dubey.