Environmental Science/Climate Change & Mitigation

Current Advances in Biodiversity, Conservation and Environmental Sciences

Data Science and AI for Climate Change
Building a Sustainable Future

Editors: D. Vetrithangam, PhD
Puneet Kumar, PhD
Arulmurugan Ramu, PhD

Data Science and AI for Climate Change

In Production
Pub Date: Forthcoming February 2027
Hardback Price: $190 USD | £150 UK
Hard ISBN: 9781779646774
E-Book ISBN: 978-1-77964-678-1
Pages: Est. 354 pp w index
Binding Type: Hardback / ebook
Series: Current Advances in Biodiversity, Conservation and Environmental Sciences
Notes: 13 color and 70 b/w illustrations

Climate change has moved far beyond scientific debate. It is now one of the most significant challenges facing our planet, affecting ecosystems, economies, and the way we live. Its complexity demands more than observation or simulation; it requires intelligence.

This new book, Data Science and AI for Climate Change: Building a Sustainable Future, explores the powerful intersection of computation and climate. It discusses adaptive, data-driven approaches to uncover hidden climatic trends, forecast disruptions, and guide practical, sustainable decisions. The book explores how artificial intelligence, machine learning, and data science can act not just as analytical tools but as catalysts for environmental resilience. The chapters demonstrate how these technologies can address challenges such as temperature forecasting, renewable-energy optimization, carbon management, and climate-resilient urban planning. It highlights the potential of intelligent algorithms to predict temperature and rainfall, optimize carbon and land resource management, and create more sustainable urban environments. This book examines how ethically guided data and technology can foster sustainability, moving beyond mere innovation to address accountability.

With chapters written by authors with diverse backgrounds, including environmental science, geospatial analytics, computer engineering, and sustainability policy, this volume is written for a broad audience: researchers, engineers, policymakers, students, and educators, as well as anyone curious about how data and AI can contribute to a cleaner, more sustainable world. It connects theory with practice, showing how digital intelligence can drive measurable, lasting change.

CONTENTS:
Preface

1. Simulating Climate Scenarios with Advanced Analytics: From Past Trends to Future Projections
Arul Kumar Natarajan

2. Predicting the Cascading Effects of Climate Change Through Network Analysis
Kanthavel R., Adline Freeda R., and Dhaya R.

3. Forecasting Temperature Change Prediction in Asia, Europe, and North America for the Next Decade: A Time Series Analysis Using ARIMA, SARIMA, AND ETS
Subhra Prosun Paul, Rezwanul Haque, Abid Haider, and Aloke Kumar Saha

4. Sustainable Wind Energy Optimization Using Reinforcement Learning Techniques
D. Vetrithangam, Puneet Kumar, and Jaspreet Singh Batth

5. AI and Data Science-Driven Carbon Management: A Multifaceted Approach
Guneet Kaur, Amandeep Kaur, and Gurvinder Singh

6. AI and Data Science for Sustainable Land Management: Balancing Carbon Sequestration and Food Security
Ruby Celsia Arul Selvaraj, Kowsalya Karuppaiah, Valentine Nlebedim, and Parthiban Karuppiah

7. Building Climate-Resilient Cities with Geospatial Analytics: Urban Planning for a Changing World
Filippo Verre and Arulmurugan Ramu

8. Building Predictive Model for Rainfall Forecast Using Machine Learning
Shamik Palit, Chandrima Sinha Roy, and Debashis Das

9. Mitigating Sea-Level Rise with AI: Optimizing Coastal Defences and Adaptation Strategies
Muhammad Aqib, Aneta Ismail, Hira Fatima, Zeeshan Sadiq, and Muhammad Arif

10. The Ethical Landscape: Considerations for Fairness, Transparency, and Accountability in AI for Climate Change
Rose Oluwaseun Adetunji and Kabir Olorede

11. AI for Industry 4.0: Monitoring, Optimization, and Enforcement for Green Production
Aditya Vardhan, Sagar Sharma, and R. Venkatesh

12. Artificial Intelligence-Based Future Climates of Business Trends in the Financial System
Patrali Pradhan and Santanu Koley

13. AI-Driven Sustainability for a Better Tomorrow: A Case Study of Vadodara, a Region in India
Jay Amin, Shobhit Chaturvedi, Naimish Bhatt, and Garlapati Nagababu

Index


About the Authors / Editors:
Editors: D. Vetrithangam, PhD
Professor, Department of Computer Science & Engineering, Chandigarh University, Punjab, India

D. Vetrithangam, PhD, is working as a Professor in the Department of Computer Science & Engineering at Chandigarh University, Punjab, India. She has more than 16 years of experience in teaching and research and has published more than 57 research papers in various Scopus- and SCI-indexed journals and conferences. She is currently working in image processing, deep learning, machine learning, the Internet of Things, and remote sensing for various applications, including medical imaging, disease detection and prediction, cancer detection, soil moisture, and climate change. She has published books and book chapters, holds 12 published patents, and has more than five granted patents. She also guides PhD and ME research scholars working in machine learning, deep learning, explainable AI, and wireless sensor networks.

Puneet Kumar, PhD
Associate Director and Professor, Department of Computer Science & Engineering, Chandigarh University, Punjab, India

Puneet Kumar, PhD, is currently working as an Associate Director and Professor in the Department of Computer Science & Engineering at Chandigarh University, Punjab, India. He believes in the philosophy of interdisciplinary research and has more than 20 years of experience across teaching, research, industry, and academic administration. His primary research interests include data science, machine learning, and e-government. He has published research papers and articles in national and international journals, most of which are related to machine learning. He also guides PhD and ME research scholars working in machine learning and explainable AI. He has published edited books titled Artificial Intelligence and Global Society and The Stances of e-Government: Policies, Processes and Technologies.

Arulmurugan Ramu, PhD
Associate Professor, School of Mathematical and Computer Sciences Engineering, Heriot-Watt University, Kazakhstan

Arulmurugan Ramu, PhD, is an Associate Professor at the School of Mathematical and Computer Sciences Engineering, Heriot-Watt University, Kazakhstan. His research focuses on the automatic interpretation of images and related problems in machine learning and optimization. His primary research interest is in vision, particularly high-level visual recognition. He has authored more than 72 papers published in computer vision and machine learning conferences and journals. He holds a PhD in Information and Communication Engineering from Anna University, Chennai; an MTech in Information Technology from Anna University of Technology; and a BTech in Information Technology. He has guided many PhD research scholars in the area of image processing using machine learning.




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