Autorentext

Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 13 years of teaching experience. He has authored seven books, edited 51 books, and published more than 170 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.

Pooja Dixit is an Assistant Professor in the Department of Computer Science at Shri Ratanlal Kanwarlal Patni Girls' College, Kishangarh, India. With more than seven years of academic teaching and two years of research experience, she has published more than 25 research papers in reputed journals, books, and conferences. Her research interests include artificial intelligence, machine learning, and data mining.

Pramod Singh Rathore, PhD, is in the Department of Computer and Communication Engineering at Manipal University Jaipur, India with more than 13 years of academic experience. He has published more than 85 papers in reputable, peer-reviewed national and international journals, books, and conferences. His research interests include NS2, computer networks, machine learning, and database management systems.

Arun Lal Srivastav, PhD is an Associate Professor in the School of Engineering and Technology at Chitkara University. He has published more than 100 research papers in various prestigious journals, conferences, and book chapters and edited many internationally published books. His research interests include water quality surveillance, climate change, water treatment, river ecosystems, soil health maintenance, engineering education, phytoremediation, and waste management.

Ashutosh Kumar Dubey, PhD is an Associate Professor in the Department of Computer Science at in the School of Engineering and Technology at Chitkara University with more than 16 years of experience. He has authored and edited 20 books and published more than 80 articles in peer-reviewed international journals and conference proceedings. His research interests encompass machine learning, renewable energy, health informatics, nature-inspired algorithms, cloud computing, and big data.

Klappentext

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.

Inhalt

Preface xv

1 Introduction to Microplastic and the Role of AI 1
Pooja Dixit, Shaloo Dadheech, Priya Batta and Neeraj Bhargava

1.1 Introduction 2
1.2 Microplastic Distribution and Pathways 5
1.3 Current Methods of Microplastic Detection 8
1.4 Role of Artificial Intelligence (AI) in Microplastic Research 12
1.5 Case Studies and Applications 16
1.6 Challenges and Limitations 18
1.7 Future Directions 20
1.8 Conclusion 21

2 A CNN-ViT Hybrid Deep Learning Architecture for Accurate Microplastic Detection 23
B. Dhanalaxmi, B. Saritha, P. Punitha, G. Jagan Naik and B. Anupama

2.1 Introduction 24
2.2 Literature Review 26
2.3 Proposed Mythology 29
2.4 Result and Discussion 31
2.5 Concluding Remarks and Future Scope 33

3 XAI for Decision Support in Microplastic Pollution Management 37
Srinibas Pattanaik, Sachin Ahuja, Sartajvir Singh Dhillon, Jasneet Chawla, Deeksha Sonal and Alessandro Vinciarelli

3.1 Introduction 38
3.2 Causes and Consequences and Effects of Microplastic Pollution 40
3.3 The Application of AI in Management of the Environment 42
3.4 XAI Frameworks are Flexible and for the Micro Plastic Environmental Management and the Summary to Explainable Artificial Intelligence 43
3.5 Application and Case Studies of XAI Microplastic Pollution Management 45
3.6 The Utilization of Machine Learning with Explainable AI (XAI) Regarding Decision Support Systems 48
3.7 Futures Directions and Challenges of Explainable AI with Microplastic Pollution 49
3.8 Conclusion 51

4 AI-Driven Technologies in Mitigation of Microplastic Pollution 55
Lata Rani, Hurmat, Deepa Singh, Babu Bharman, Arun Lal Srivastav, Jyotsna Kaushal, Komal Thapa and Neha Kanojia

4.1 Introduction 56
4.2 AI Assisted Detection Techniques for the Microplastic 60
4.3 Application of AI in Microplastic Pollution Control 71
4.4 Conclusion 74

5 AI Driven Optical Imaging and Spectroscopic Techniques 83
Muchukota Sushma, Mekkanti Manasa Rekha, Ramya C. V. and Zaid Khan

List of Abbreviations 84
5.1 Introduction 84
5.2 Fundamentals of Optical Imaging and Spectroscopic Techniques 90
5.3 AI Innovations in Microplastic Detection 92
5.4 Applications in Real-Time Monitoring 94
5.5 Case Studies in AI-Driven Microplastic Detection 95
5.6 Challenges in AI-Driven Microplastic Monitoring 97
5.7 Future Directions 99
5.8 Conclusion 101

6 Integrating AI with Advanced Sensor Technologies for Real-Time Monitoring 109
Avnish Chauhan, Shivam Attri, Aanchal Saklani, Prabhat K. Chauhan, Man Vir Singh, Vishal Rajput, Muneesh Sethi and Samuele Barrili

6.1 Introduction 110
6.2 Bibliographic Study 111
6.3 AI-Enabled Sensor Technologies for Microplastic Detection 113
6.4 Challenges and Future Prospects 120
6.5 Conclusion 122

7 Machine Learning for Microplastic Source and Pathway Prediction 127
Vanshika and Neetu Rani

7.1 Introduction 128
7.2 Microplastic Sources and Pathways: An Overview 130
7.3 Data Acquisition and Preprocessing 132
7.4 Machine Learning Approaches for Microplastic Modeling 134
7.5 Model Development and Validation 137
7.6 Case Studies and Real-World Implementations 138
7.7 Visualization and Decision Support 138
7.8 Challenges and Ethical Considerations 142
7.9 Conclusion and Future Scope 143

8 Big Data Analytics in Mapping the Global Microplastic Distribution 147
Prasann Kumar

8.1 Introduction 148
8.2 Data Sources for Microplastic Mapping 152
8.3 Big Data Techniques in Microplastic Analytics 155
8.4 Challenges in Big Data for Microplastic Studies 159
8.5 Case Studies 163
8.6 Applications and Implications 166
8.7 Future Directions 170
8.8 Conclusion 173
8.9 Acknowledgement 174

**9 Automation in S…