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.
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.
Audience
Researchers, civil, structural, and environmental engineers, policymakers, and graduate students involved in disaster management, sustainable development, AI, and data science.
Table of Contents
Preface xvii
1 Introduction to Sustainable Development and Disaster Management 1
Rajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash
1.1 Introduction 2
1.2 Sustainable Development 9
1.3 Disaster Management 10
1.4 Role of AI in Sustainable Development 12
1.5 Role of AI in Disaster Management 15
1.6 Integration of AI in Sustainable Disaster Management 17
1.7 Conclusion 21
2 Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence–Based Methods 25
Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.
3 AI Applications in Earthquake Resistance Using Change in Structural Design 61
E. Nirmala, M. Suresh and Sankar Muthu Paramasivam
3.1 Introduction 62
3.2 Review of Literature 63
3.3 Proposed Techniques 64
3.4 AI- and ML-Based Techniques 70
3.5 Conclusion and Future Work 74
4 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models 79
Rajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar
4.1 Introduction 80
4.2 Related Works 82
4.3 Dataset Description 83
4.4 Methodology 84
4.5 Model Evaluation Indicators 88
4.6 Experimental Results 89
4.7 Discussion 93
4.8 Conclusion 94
5 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives 99
Geetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya
5.1 Introduction 100
5.2 Importance of Intelligent Transportation Systems in Cyclone Resilience 101
5.3 Early Warning Systems 103
5.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management 106
5.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas 108
5.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control 111
5.7 Conclusion 117
6 AI-Enhanced Risk Assessment and Mitigation for Mass Movements 121
G. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi
6.1 Introduction 122
6.2 Understanding Mass Movements 123
6.3 Traditional Risk Assessment and Mitigation Methods 124
6.4 The Role of AI in Risk Assessment 125
6.5 AI-Enhanced Mitigation Strategies 127
6.6 Challenges and Ethical Considerations 129
6.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management 130
6.8 Case Studies in AI-Enhanced Mass Movement Management 132
6.9 Conclusions 134
7 Distributed AI Systems for Disaster Response and Recovery 137
Ravikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar
7.1 Introduction 138
7.2 Technology Applied in Critical Cases 141
7.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology 145
7.4 ML and Deep Learning Methods: An Overview 146
7.5 Phases of Disaster Management 151
7.6 Disaster Management and Disaster Resilience 155
7.7 Applications of AI for Disaster Management 156
7.8 AI Applications in Disaster Mitigation 156
7.9 Conclusion 157
8 Intelligent Reasoning and Decision‑Making in Disaster Scenarios 163
Sreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.
8.1 Introduction 164
8.2 Types of Natural Disasters 165
8.3 Impact of Natural Disasters 167
8.4 Decision-Making in a Disaster Scenario 170
8.5 AI/Machine Learning in Decision-Making of Disaster Scenario 174
8.6 AI Methods for Disaster Prediction 179
8.7 AI Methods to Analyze the Impact of Disasters 195
8.8 AI/ML Methods in Providing Precautionary Measures 210
8.9 Intelligent Reasoning 214
8.10 Conclusion 219
9 AI Applications in Real-Time Intelligent Automation 229
M. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun Li
9.1 Introduction 230
9.2 Related Works 233
9.3 Proposed Methods 235
9.4 Conclusion and Future Perspectives 248
10 Knowledge Management and Processing in Disaster Management 251
R. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan
10.1 Introduction 252
10.2 Knowledge Management in Disaster Management 255
10.3 Integration of AI in Disaster Management 265
10.4 Challenges and Ethical Considerations 275
10.5 Future Prospects and Innovations 284
10.6 Conclusion 294
11 Perception Technologies for Disaster Situations 301
Ganesh Nataraj, K. Mohanasundaram and S. Ramesh Babu
11.1 Introduction 302
11.2 Understanding Disaster Situations 303
11.3 Role of Perception Technologies 305
11.4 Categories of Perception Disaster Technologies 306
11.5 Conclusion 311
12 Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective 315
Mithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.
12.1 Introduction 316
12.2 Related Works 318
12.3 Methodology 320
12.4 Experiments and Result Discussion 323
12.5 Conclusion 328
13 An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security 333
Indra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.
13.1 Introduction 334
13.2 Evolution of Autonomous Vehicles 335
13.3 Role of AI in Autonomous Systems 336
13.4 Challenges of Artificial Intelligence in Autonomous Systems 344
13.5 Blockchain Security Measures for Autonomous Vehicles 346
13.6 List of Software/Tools, Design Techniques and Programming Languages for Autonomus Systems 349
13.7 Conclusion 353
14 Industrial Experiences in Crop Cultivation Using AI for Disaster Management 357
Sagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee
14.1 Introduction 358
14.2 Related Work 360
14.3 Proposed Framework 362
14.4 Performance Analysis 364
14.5 Conclusion 366
15 A Comprehensive Review on Robotics in Disaster Response and Recovery 369
J. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi
15.1 Introduction 370
15.2 Disaster Response Robotics 372
15.3 Robotics in Disaster Recovery 376
15.4 Future Directions 387
15.5 Conclusion 390
References 391
Index 393
