Viticulture & Enology

Advancements in AI and ML for Early Warning Systems in Natural Disaster Detection
Future Trends and Innovations

Editors: Jay Kumar Pandey, PhD
Mritunjay Rai, PhD

Advancements in AI and ML for Early Warning Systems in Natural Disaster Detection

In Production
Pub Date: Forthcoming April 2026
Hardback Price: $220 US | £170 UK
Hard ISBN: 9781779643964
E-Book ISBN: 978-1-77964-397-1
Pages: Est 502pp w index
Binding Type: hardbound / ebook
Notes: 16 color and 74 b/w illustrations

As extreme weather events become more frequent due to climate change, the need for more effective disaster management solutions has never been greater. This new book, Advancements in AI and ML for Early Warning Systems in Natural Disaster Detection: Future Trends and Innovations, addresses the growing role of artificial intelligence and machine learning in building resilient, adaptive frameworks for natural disaster management. The book explores how these technologies are being employed to revolutionize early warning systems (EWS), aiding in the detection, prediction, and mitigation of disaster risks as well as in post-disaster recovery efforts. This book examines these solutions through a comprehensive review of digital twin technology, intelligent reasoning, IoT integration, data fusion, sentiment analysis, and more. The chapters delve into how AI-driven models simulate human-like problem-solving approaches and reveal how AI empowers local communities with predictive insights, aiding in timely, impactful interventions.

Chapters demonstrate how digital replicas of ocean environments can play a pivotal role in early warning systems, climate adaptation, and sustainable resource management; and highlight how AI can merge data from various sources such as satellite imagery, social media, IoT sensors, and environmental data to provide a holistic perspective on disaster impact, encompassing both physical damage and emotional responses of affected communities. The book explores an IoT-based disaster management system with explainable AI (XAI), representing a new frontier in EWS and offering transparent and understandable insights into AI-driven decisions. Other chapters on early warning systems and AI-driven communication platforms illustrate the importance of integrating AI with citizen participation, enhancing community engagement, and improving the efficacy of disaster response efforts.

The impact of Industry 6.0 on climate change and natural disasters is explored through an analysis of evolving industrial practices, highlighting the importance of sustainable strategies and AI-driven disaster resilience. The latest machine learning algorithms for disaster detection are discussed, focusing on everything from urban flooding to earthquake risk assessment through fuzzy AI techniques.

In addressing post-disaster recovery efforts, the book emphasizes AI and ML’s roles in damage assessment and recovery coordination. With technologies like satellite imagery, remote sensing, and crowdsourced data, AI can help restore affected areas efficiently and ethically. Finally, this book underscores the socio-economic and psychological implications of AI-driven disaster detection technologies, emphasizing the importance of trust, transparency, and human-centric approaches in AI systems.

By bridging traditional and AI-enhanced resilience strategies, Advancements in AI and ML for Early Warning Systems in Natural Disaster Detection provides a forward-looking perspective on disaster management. The collective insights aim to inspire further research, encouraging the development of comprehensive, adaptive solutions to address the increasing frequency and intensity of natural disasters worldwide.

CONTENTS:
Preface

1. Digital Twins for Ocean Resilience and Blue Resources Sustainability Through Enhanced Early Warning Systems and Climate Change Adaptation
Elegbde, Isa Olalekan, Orilonishe Summayah, Anabela Oliveira, Marta Rodrigues, Olarinmoye, Tosin, and Ruqoyah Sanni-Matti

2. Intelligent Reasoning and Decision-Making in Disaster Scenarios
A. Arul, M. Kathirvelu, S. Thivaharan, and P. Yogeswari

3. Combating Natural Disasters in Nigeria with Artificial Intelligence
Basil Osayin Daudu, Esther Besty Anaiye, Patrick Emmanuel Adejo, and Amodu Salisu Ameh

4. Deep Learning Techniques for Multimodal Data Fusion in Disaster Detection
Madan Mohan Tito Ayyalasomayajula, Sailaja Ayyalasomayajula, and Jay Kumar Pandey

5. Natural Disasters and Their Impact on Emotional Sentiment in Social Media
Shivani Singh, Jay Kumar Pandey, Mritunjay Rai, and Abhishek Kumar Saxena

6. Sentinel: An IoT-Based Disaster Management System with Explainable AI
Manas Kumar Yogi and A. S. N. Chakravarthy

7. Early Warning Systems for Natural Disaster Using AI to Enhance Emergency Communication and Citizen Participation
Fuzel Ahamed Shaik, Getnet Demil, and Mourad Oussalah

8. Investigating Causes, Effects, and Impact of Industry 6.0 on Natural Disaster and Climate Change
Monika Singh T., Kishor Kumar Reddy C., and Marlia Mohd Hanafiah

9. Harnessing Artificial Intelligence and Machine Learning Techniques for Advanced Disaster Management
Abhishek, Mritunjay Rai, Abhishek Kumar Saxena, and Jay Kumar Pandey

10. Machine Learning Algorithms for Disaster Detection
Mahesh Kumar Singh

11. Advancing Natural Disaster Warning Systems from Tradition to AI-Enhanced Resilience
Emy Santo, G. Usha, and H. Karthikeyan

12. Integrating AI and ML with IoT for Real-Time Flood Detection and Monitoring: A Comprehensive Approach
Ajoy Kanti Das, Nandini Gupta, Carlos Granados, Tahir Mahmood, and Suman Das

13. Integration of AI And ML In Context of IoT for Natural Disaster Management System: A Comprehensive Survey
Sandeep Bhatia, Neha Goel, Soniya Verma, Vinay Kumar Ahlawat, and Hardeep Singh Dhillon

14. AI and ML in Disaster Detection: Socio-Economic and Psychological Impacts
Durgeshwary Kolhe and Arshad Bhat

15. Leveraging Artificial Intelligence and Machine Learning for Enhanced Natural Disaster Management: A Comprehensive Review
Sushma Malik and Anamika Rana

16. The Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection
Petros Chavula, Fredrick Kayusi, Gilbert Lungu, and Agnes Uwimbabazi

17. Role of Artificial Intelligence and Machine Learning in Post-Disaster Recovery Efforts
Shipra Shukla

18. Integrating Data Frontiers: Revolutionizing Natural Disaster Preparedness with Emerging Technologies
Sumanta Das, Malini Roy Choudhury, Bhagyasree Chatterjee, Rasmoni Karak, Sujan Mandal, Mahadev Bera, and Suman Dutta

Index


About the Authors / Editors:
Editors: Jay Kumar Pandey, PhD
Assistant Professor, Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, U.P., Barabanki, India

Jay Kumar Pandey, PhD, is currently working as an Assistant Professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, U.P., Barabanki, India. Dr. Pandey completed his PhD and earned his MTech with specialization in Power Control (Instrumentation), and was awarded his MBA in Finance and Marketing. He has 15 years of teaching and research experience and has published more than 30 research papers in national and international journals and conferences as well as book chapters and books with Wiley, CRC Press, Nova Science Publishers, Taylor & Francis, Springer, Elsevier, Intech Open Access, and IGI Global. Dr. Pandey is also Editor of the Journal of Technology Innovations and Energy (United States) and is a reviewer for conferences, book chapters, and journals, including the Journal of Supercomputing, Journal of Security and Communication Networks, Journal of Biomimetics, Biomaterials and Biomedical Engineering, Advanced Engineering Forum, etc. His subjects of interest are related to biomedical and healthcare, image processing, digital electronics, machine learning, and solar PV. He is an active member of the Institution of Electronics and Telecommunication Engineers and the International Association of Engineers as well as a global member of the Internet Society and life member of the Institute for Systems and Technologies of Information, Control and Communication and The Institute of Engineering and Technology, UK.

Mritunjay Rai, PhD
Assistant Professor, Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, U.P., Barabanki, India

Mritunjay Rai, PhD, is currently working as an Assistant Professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, U.P., Barabanki, India. He received his PhD from Indian Institute of Technology (Indian School of Mines), Dhanbad, India, in Electrical Engineering with a specialization in Image Processing; his Master of Engineering with distinction from the Birla Institute of Technology, Mesra, Ranchi, in Instrumentation and Control; and his BTech in Electronics and Communication Engineering from Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, India. Dr. Rai is an active researcher and has published several papers in SCI-indexed Q2 journals and at international and national conferences in his field. His areas of interest lie in image processing, medical image processing, healthcare systems, artificial intelligence, machine learning, deep learning, IoT, communication, speech processing, and robotics and automation. He has contributed many chapters to books published by Intech Open Access, CRC Press, IGI Global, and Elsevier. He is an editor of books published by reputed publishers, including Wiley, Apple Academic Press, Nova Science Publishers and IGI Global. Currently, he is actively working on various statistical models to improve the efficiency of surveillance systems.




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