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Integrating AI for Sustainable Disaster Management

Building Resilience and Preventing Catastrophes

Edited by Palanichamy Naveen, R. Maheswar, K. Mohanasundaram, T. Rajasekaran and S. Arivazhagan
Copyright: 2026   |   Expected Pub Date:2026/01/30
ISBN: 9781394271573  |  Hardcover  |  
416 pages

One Line Description
Future-proof your disaster management strategy with this essential,
multidisciplinary guide that shows how cutting-edge AI technologies can be practically integrated to enhance early warning systems, save lives, and build long-term community resilience.

Audience
Researchers, civil, structural, and environmental engineers, policymakers, and graduate students involved in disaster management, sustainable development, AI, and data science.

Description
By bridging the fields of AI, engineering, and sustainable development, this book offers a comprehensive, multidisciplinary approach to disaster management. It provides valuable insights for researchers, practitioners, and policymakers on how to integrate AI to improve decision-making, enhance infrastructure design, and promote long-term sustainability. This book explores the transformative role of artificial intelligence in enhancing disaster resilience and promoting sustainable disaster management practices. The book delves into cutting-edge AI technologies, such as machine learning, deep learning, robotics, and big data analytics, showcasing their potential to improve risk assessment, early warning systems, and real-time disaster response. It focuses on practical applications for mitigating natural hazards like earthquakes, cyclones, and mass movements, providing real-world case studies and successes that demonstrate how AI can save lives, reduce economic loss, and strengthen community resilience. The practical examples and forward-looking perspectives explored in this book make it a crucial resource for anyone working to mitigate the impacts of natural disasters and build a more resilient, sustainable future.
Readers will find the volume:
• Explores how artificial intelligence enhances risk assessment, early warning systems, and realtime disaster response;
• Provides practical insights through detailed examples of AI applications in earthquakes, cyclones, and mass movement management;
• Demonstrates how AI can support sustainable practices and align with global development goals to build resilient communities;
• Provides comprehensive coverage, combining expertise from AI, engineering, sustainability development, and disaster management practitioners;
• Introduces the latest AI techniques, including IoT, big data, deep learning, and robotics, for effective disaster prevention and recovery.

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Author / Editor Details
Palanichamy Naveen, PhD is an Associate Professor in the Department of Electronics and Communication Engineering at the Dr. N.G.P. Institute of Technology, India with more than 13 years of experience. He has published more than 25 papers in international journals and conferences.

R. Maheswar, PhD is the Director In-Charge in the Centre for Research and Development, Head of the Centre for IoT and AI, and a Professor in the Department of Electronics and Communication Engineering at the KPR Institute of Engineering and Technology, India, with more than 21 years of teaching experience. He has published more than 70 papers in international journals and conferences.

K. Mohanasundaram, PhD is a Professor and the Head of the Electrical and Electronics Engineering Department at the KPR Institute of Engineering and Technology, India. He has completed two projects sponsored by the Indian government’s Department of Science and Technology.

Rajasekaran Thangaraj, PhD is a Professor at the Nandha College of Engineering, India. He has published 25 articles in reputed international journals and presented 18 papers at various international conferences.

S. Arivazhagan, PhD is an Assistant Professor in the Mechanical Department at the KPR Institute of Engineering and Technology, India with more than 10 years of experience. He has published six journal articles and eight patents, two of which are granted.

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Table of Contents
Preface
1. Introduction to Sustainable Development and Disaster Management
Rajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash
1.1 Introduction
1.1.1 Overview of Sustainable Development
1.1.1.1 Core Concepts of Sustainable Development
1.1.1.2 Historical Context of Sustainable Development
1.1.1.3 Principles of Sustainable Development
1.1.1.4 Challenges and Opportunities in Achieving Sustainable Development
1.1.2 Importance of Disaster Management
1.1.2.1 Definition and Scope of Disaster Management
1.1.2.2 Phases of Disaster Management
1.1.2.3 Types of Disasters
1.1.2.4 Challenges in Disaster Management
1.1.2.5 Importance of Effective Disaster Management
1.1.2.6 Case Studies of Disaster Management
1.1.3 Intersection of AI, Sustainable Development, and Disaster Management
1.2 Sustainable Development
1.2.1 Definition and Principles
1.2.2 Historical Context and Evolution
1.2.3 Goals and Global Initiatives (SDGs)
1.3 Disaster Management
1.3.1 Definition and Types of Disasters
1.3.2 Phases of Disaster Management
1.3.3 Challenges in Traditional Disaster Management Approaches
1.4 Role of AI in Sustainable Development
1.4.1 AI Technologies and their Applications
1.4.2 Case Studies of AI in Sustainable Development
1.5 Role of AI in Disaster Management
1.5.1 AI Technologies in Disaster Prediction and Early Warning
1.5.2 AI in Disaster Response and Recovery
1.5.3 Case Studies of AI in Disaster Management
1.6 Integration of AI in Sustainable Disaster Management
1.6.1 Benefits of AI Integration
1.6.2 Framework for AI Integration
1.6.2.1 Identifying Key Areas for AI Application
1.6.2.2 Ensuring Data Accessibility and Quality
1.6.2.3 Fostering Collaboration Among Stakeholders
1.6.2.4 Addressing Ethical Considerations
1.6.2.5 Ensuring Transparency
1.6.3 Challenges and Ethical Considerations
1.7 Conclusion
References
2. Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence–Based Methods
Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.
Introduction to Earthquake Risk Assessment
Understanding Seismic Hazards
Data Source of Earthquake Risk Assessment
Scenario of Earthquake Incidents of the World
Scenario of Earthquake Incidents of India
Brief Overview of Earthquake Incidents in India
Traditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis
Seismic Hazard Mapping
Ground Motion Prediction
Fault Rupture Hazard Analysis
Site-Specific Studies
Building Vulnerability Assessment
Organizations for Earthquake Risk Assessment and Predictions
Earthquake Risk Assessment Using Artificial Intelligence
Prediction of Earthquake Using AI
Algorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms
Machine Learning Algorithms
Methods for Earthquake Risk Assessment and Prediction Using AI
Pattern Recognition in Seismic Data
Anomaly Detection
Earthquake Forecasting Model
Data Fusion and Integration
Damage and Impact Assessment
Real-Time Monitoring
Early Warning Systems
Risk Mitigation
Resilience Planning
Predictive Modeling for Earthquake Forecasting Using AI
Integration of AI Techniques in Seismic Hazard Analysis
Construction Practices and Urban Planning for Earthquake Assessment Using AI
Future Scope of Earthquake Risk Assessment and Prediction Using AI
Conclusion
References
3. AI Applications in Earthquake Resistance Using Change in Structural Design
E. Nirmala, M. Suresh and Sankar Muthu Paramasivam
3.1 Introduction
3.2 Review of Literature
3.3 Proposed Techniques
3.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes
3.3.2 Earthquake Prediction Using ANN
3.3.3 AI–Neural Network–Based Earthquake Prediction
3.3.4 AI-Based Dynamic Interpretation Network (DIN)–Multilayer Propagation Algorithm for Earthquake Prediction
3.4 AI- and ML-Based Techniques
3.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms
3.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example
3.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake
3.4.4 Seismic Advancement of Prevailing Masonry Structures
3.5 Conclusion and Future Work
Bibliography
4. Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models
Rajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar
4.1 Introduction
4.2 Related Works
4.3 Dataset Description
4.3.1 Dataset Collection
4.3.2 Dataset Preprocessing
4.4 Methodology
4.4.1 YOLO
4.4.2 YOLOv3
4.4.3 Tiny-YOLOv4
4.4.4 YOLOv5
4.5 Model Evaluation Indicators
4.6 Experimental Results
4.7 Discussion
4.8 Conclusion
References
5. Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives
Geetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya
5.1 Introduction
5.2 Importance of Intelligent Transportation Systems in Cyclone Resilience
5.3 Early Warning Systems
5.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management
5.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas
5.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control
5.7 Conclusion
References
6. AI-Enhanced Risk Assessment and Mitigation for Mass Movements
G. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi
6.1 Introduction
6.2 Understanding Mass Movements
6.3 Traditional Risk Assessment and Mitigation Methods
6.4 The Role of AI in Risk Assessment
6.5 AI-Enhanced Mitigation Strategies
6.6 Challenges and Ethical Considerations
6.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management
6.8 Case Studies in AI-Enhanced Mass Movement Management
6.9 Conclusions
References
7. Distributed AI Systems for Disaster Response and Recovery
Ravikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar
7.1 Introduction
7.2 Technology Applied in Critical Cases
7.2.1 Disaster Management Architecture
7.2.2 Proposed Framework
7.2.3 Disaster Management Ontology
7.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology
7.4 ML and Deep Learning Methods: An Overview
7.4.1 Convolutional Neural Network
7.4.2 LSTM
7.4.3 Support Vector Machine
7.4.4 ML/DL Methods for Disaster and Hazard Prediction
7.4.5 ML/DL Methods for Risk and Vulnerability Assessment
7.4.6 ML/DL Methods for Disaster Detection
7.4.7 ML/DL Methods for Disaster Monitoring
7.4.8 ML/DL Methods for Damage Assessment
7.5 Phases of Disaster Management
7.5.1 Prediction
7.5.2 Detection
7.5.3 Response
7.5.4 Recovery
7.5.5 Before Disaster
7.5.5.1 Risk Assessment
7.5.5.2 Mitigation
7.5.5.3 Prevention
7.5.5.4 Prediction
7.5.5.5 Detection
7.5.6 During Disaster
7.5.6.1 Preparation
7.5.6.2 Management
7.5.6.3 Response
7.5.7 After Disaster
7.5.7.1 Recovery
7.5.7.2 Monitoring
7.5.7.3 Lessons Learned
7.6 Disaster Management and Disaster Resilience
7.7 Applications of AI for Disaster Management
7.8 AI Applications in Disaster Mitigation
7.9 Conclusion
References
8. Intelligent Reasoning and Decision‑Making in Disaster Scenarios
Sreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.
8.1 Introduction
8.2 Types of Natural Disasters
8.3 Impact of Natural Disasters
8.4 Decision-Making in a Disaster Scenario
8.4.1 Disaster Prediction
8.4.2 Decision-Making in Analyzing the Impact of Disaster
8.4.3 Disaster Precautions and Measures
8.4.4 Benefits of Decision-Making in Disaster Scenario
8.4.5 Technology in Decision-Making Process of a Disaster
8.5 AI/Machine Learning in Decision-Making of Disaster Scenario
8.5.1 AI/ML in Predisaster Stage
8.5.2 AI/ML in During Disaster Stage
8.5.3 AI/ML in Postdisaster Stage
8.6 AI Methods for Disaster Prediction
8.6.1 Cyclone
8.6.2 Drought
8.6.3 Earthquake
8.6.4 Floods
8.6.5 Landslides
8.7 AI Methods to Analyze the Impact of Disasters
8.7.1 Cyclone
8.7.2 Drought
8.7.3 Earthquake
8.7.4 Floods
8.7.5 Landslide
8.8 AI/ML Methods in Providing Precautionary Measures
8.9 Intelligent Reasoning
8.10 Conclusion
References
9. AI Applications in Real-Time Intelligent Automation
M. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun Li
9.1 Introduction
9.2 Related Works
9.3 Proposed Methods
9.3.1 Use of Drones in Disaster Management
9.3.1.1 Understanding Drone Technology
9.3.1.2 Components and Functionality
9.3.1.3 Types and Classifications
9.3.1.4 Applications
9.3.1.5 Challenges and Future Trends
9.3.1.6 Drone Applications in Earthquake Disaster Response
9.3.1.7 Rapid Damage Assessment
9.3.1.8 Search and Rescue Operations
9.3.1.9 Communication and Coordination
9.3.1.10 Environmental Monitoring and Mapping
9.3.2 Flood Disaster Management Using the Flood Detection Secure System
9.3.2.1 Terminologies in FDSS
9.3.2.2 The Process of FDSS
9.3.3 Flood Management Using AI and IoT
9.3.3.1 Architecture
9.4 Conclusion and Future Perspectives
References
10. Knowledge Management and Processing in Disaster Management
R. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan
10.1 Introduction
10.1.1 Importance of Knowledge Management
10.1.2 Role of AI
10.2 Knowledge Management in Disaster Management
10.2.1 Data Collection
10.2.2 Information Processing
10.2.3 Knowledge Dissemination
10.2.4 Decision Support Systems
10.3 Integration of AI in Disaster Management
10.3.1 Machine Learning Applications
10.3.2 Natural Language Processing
10.3.3 Predictive Analytics
10.4 Challenges and Ethical Considerations
10.4.1 Data Privacy
10.4.2 Bias and Reliability
10.4.3 Resource Allocation
10.5 Future Prospects and Innovations
10.5.1 Technological Advances
10.5.2 Integration with Existing Systems
10.5.3 Global Collaboration
10.6 Conclusion
10.6.1 Summary of Key Points
10.6.2 Call to Action
10.6.3 Future Vision
References
11. Perception Technologies for Disaster Situations
Ganesh Nataraj, K. Mohanasundaram and S. Ramesh Babu
11.1 Introduction
11.2 Understanding Disaster Situations
11.3 Role of Perception Technologies
11.4 Categories of Perception Disaster Technologies
11.4.1 Remote Sensing and Imaging Technologies
11.4.2 Computer Vision and Image Analysis
11.4.3 Internet of Things (IoT) Sensors
11.4.4 Data Fusion and Integration
11.4.5 Human-Computer Interaction and Decision Support Systems
11.4.6 Ethical and Privacy Considerations
11.4.7 Future Directions and Challenges
11.5 Conclusion
References
12. Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective
Mithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.4 Experiments and Result Discussion
12.5 Conclusion
Bibliography
13. An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security
Indra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.
13.1 Introduction
13.2 Evolution of Autonomous Vehicles
13.3 Role of AI in Autonomous Systems
13.3.1 Architecture Diagram
13.3.2 AI Algorithms for Fault Prediction and Recognition
13.3.2.1 Isolation Forest Algorithm
13.4 Challenges of Artificial Intelligence in Autonomous Systems
13.5 Blockchain Security Measures for Autonomous Vehicles
13.5.1 Secure Autonomous Vehicle Network Using Blockchain
13.6 List of Software/Tools, Design Techniques and Programming Languages for Autonomus Systems
13.6.1 Case Studies and Practical Implementations in an Autonomous System
13.6.2 Key Findings and Contributions
13.7 Conclusion
References
14. Industrial Experiences in Crop Cultivation Using AI for Disaster Management
Sagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee
14.1 Introduction
14.1.1 AI in Agriculture
14.1.2 Contribution
14.2 Related Work
14.3 Proposed Framework
14.3.1 Construction of Knowledge Graph
14.4 Performance Analysis
14.4.1 Crop Query Dataset
14.4.2 Results Discussion
14.5 Conclusion
References
15. A Comprehensive Review on Robotics in Disaster Response and Recovery
J. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi
15.1 Introduction
15.1.1 Role of Robotics in Disaster Response
15.1.2 Role of Robotics in Disaster Recovery
15.1.3 The Key Objectives of Reviewing Robotics in Disaster Response and Recovery
15.2 Disaster Response Robotics
15.2.1 Overview of Different Types of Disasters (Natural and Man-Made)
15.2.2 Robotics Technologies Used in Disaster Response
15.3 Robotics in Disaster Recovery
15.3.1 The Transition from the Response to the Recovery Phase in Disaster Management
15.3.2 The Role of Robotics in Postdisaster Recovery
15.3.3 Infrastructure Inspection and Assessment Using Drones and Ground Robots
15.3.4 Debris Clearance and Demolition with Robotic Assistance
15.3.5 Rehabilitation and Reconstruction Aided by Robotics in Construction
15.3.6 Psychological Support Through Robotic Companionship and Therapy
15.3.7 Review of Case Studies or Research Papers Demonstrating the Application and Impact of Robotics in Disaster Recovery Efforts
15.4 Future Directions
15.4.1 Exploration of Emerging Trends and Future Directions in Disaster Robotics Research
15.4.2 Recommendations for Future Research and Development Efforts to Maximize the Potential of Robotics in Disaster Response and Recovery
15.5 Conclusion
References
Index

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