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Integrating AI for Sustainable Disaster Management
Building Resilience and Preventing Catastrophes
Palanichamy Naveen (Edited by), Naveen (Author), R. Maheswar (Edited by), K. Mohanasundaram (Edited by), Rajasekaran Thangaraj (Edited by), S. Arivazhagan (Edited by)
9781394271573, Wiley
Hardback, published 13 January 2026
416 pages
28 x 19 x 2.6 cm, 0.666 kg
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: Audience Researchers, civil, structural, and environmental engineers, policymakers, and graduate students involved in disaster management, sustainable development, AI, and data science.
Preface xvii 1 Introduction to Sustainable Development and Disaster Management 1 1.1 Introduction 2 1.1.1 Overview of Sustainable Development 2 1.1.1.1 Core Concepts of Sustainable Development 2 1.1.1.2 Historical Context of Sustainable Development 3 1.1.1.3 Principles of Sustainable Development 3 1.1.1.4 Challenges and Opportunities in Achieving Sustainable Development 4 1.1.2 Importance of Disaster Management 5 1.1.2.1 Definition and Scope of Disaster Management 5 1.1.2.2 Phases of Disaster Management 6 1.1.2.3 Types of Disasters 6 1.1.2.4 Challenges in Disaster Management 6 1.1.2.5 Importance of Effective Disaster Management 7 1.1.2.6 Case Studies of Disaster Management 8 1.1.3 Intersection of AI, Sustainable Development, and Disaster Management 9 1.2 Sustainable Development 9 1.2.1 Definition and Principles 9 1.2.2 Historical Context and Evolution 9 1.2.3 Goals and Global Initiatives (SDGs) 10 1.3 Disaster Management 10 1.3.1 Definition and Types of Disasters 10 1.3.2 Phases of Disaster Management 10 1.3.3 Challenges in Traditional Disaster Management Approaches 11 1.4 Role of AI in Sustainable Development 12 1.4.1 AI Technologies and their Applications 12 1.4.2 Case Studies of AI in Sustainable Development 12 1.5 Role of AI in Disaster Management 15 1.5.1 AI Technologies in Disaster Prediction and Early Warning 15 1.5.2 AI in Disaster Response and Recovery 15 1.5.3 Case Studies of AI in Disaster Management 16 1.6 Integration of AI in Sustainable Disaster Management 17 1.6.1 Benefits of AI Integration 17 1.6.2 Framework for AI Integration 18 1.6.2.1 Identifying Key Areas for AI Application 18 1.6.2.2 Ensuring Data Accessibility and Quality 18 1.6.2.3 Fostering Collaboration Among Stakeholders 18 1.6.2.4 Addressing Ethical Considerations 19 1.6.2.5 Ensuring Transparency 19 1.6.3 Challenges and Ethical Considerations 19 1.7 Conclusion 21 References 22 2 Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence– Based Methods 25 Introduction to Earthquake Risk Assessment 26 Understanding Seismic Hazards 27 Data Source of Earthquake Risk Assessment 27 Scenario of Earthquake Incidents of the World 28 Scenario of Earthquake Incidents of India 29 Brief Overview of Earthquake Incidents in India 29 Traditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis 33 Seismic Hazard Mapping 34 Ground Motion Prediction 35 Fault Rupture Hazard Analysis 36 Site-Specific Studies 37 Building Vulnerability Assessment 37 Organizations for Earthquake Risk Assessment and Predictions 42 Earthquake Risk Assessment Using Artificial Intelligence 43 Prediction of Earthquake Using AI 44 Algorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms 45 Machine Learning Algorithms 45 Methods for Earthquake Risk Assessment and Prediction Using AI 46 Pattern Recognition in Seismic Data 46 Anomaly Detection 47 Earthquake Forecasting Model 47 Data Fusion and Integration 48 Damage and Impact Assessment 49 Real-Time Monitoring 50 Early Warning Systems 51 Risk Mitigation 52 Resilience Planning 52 Predictive Modeling for Earthquake Forecasting Using AI 54 Integration of AI Techniques in Seismic Hazard Analysis 55 Construction Practices and Urban Planning for Earthquake Assessment Using AI 56 Future Scope of Earthquake Risk Assessment and Prediction Using AI 57 Conclusion 58 References 59 3 AI Applications in Earthquake Resistance Using Change in Structural Design 61 3.1 Introduction 62 3.2 Review of Literature 63 3.3 Proposed Techniques 64 3.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes 64 3.3.2 Earthquake Prediction Using ANN 67 3.3.3 AI–Neural Network–Based Earthquake Prediction 67 3.3.4 AI-Based Dynamic Interpretation Network (DIN)– Multilayer Propagation Algorithm for Earthquake Prediction 68 3.4 AI- and ML-Based Techniques 70 3.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms 70 3.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example 71 3.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake 73 3.4.4 Seismic Advancement of Prevailing Masonry Structures 73 3.5 Conclusion and Future Work 74 Bibliography 75 4 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models 79 4.1 Introduction 80 4.2 Related Works 82 4.3 Dataset Description 83 4.3.1 Dataset Collection 83 4.3.2 Dataset Preprocessing 83 4.4 Methodology 84 4.4.1 Yolo 84 4.4.2 YOLOv 3 84 4.4.3 Tiny-YOLOv 4 85 4.4.4 YOLOv 5 87 4.5 Model Evaluation Indicators 88 4.6 Experimental Results 89 4.7 Discussion 93 4.8 Conclusion 94 References 95 5 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives 99 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 References 117 6 AI-Enhanced Risk Assessment and Mitigation for Mass Movements 121 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 References 135 7 Distributed AI Systems for Disaster Response and Recovery 137 7.1 Introduction 138 7.2 Technology Applied in Critical Cases 141 7.2.1 Disaster Management Architecture 143 7.2.2 Proposed Framework 144 7.2.3 Disaster Management Ontology 145 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 7.4.1 Convolutional Neural Network 147 7.4.2 Lstm 148 7.4.3 Support Vector Machine 148 7.4.4 ML/DL Methods for Disaster and Hazard Prediction 148 7.4.5 ML/DL Methods for Risk and Vulnerability Assessment 149 7.4.6 ML/DL Methods for Disaster Detection 150 7.4.7 ML/DL Methods for Disaster Monitoring 150 7.4.8 ML/DL Methods for Damage Assessment 150 7.5 Phases of Disaster Management 151 7.5.1 Prediction 151 7.5.2 Detection 152 7.5.3 Response 152 7.5.4 Recovery 152 7.5.5 Before Disaster 152 7.5.5.1 Risk Assessment 152 7.5.5.2 Mitigation 153 7.5.5.3 Prevention 153 7.5.5.4 Prediction 153 7.5.5.5 Detection 153 7.5.6 During Disaster 153 7.5.6.1 Preparation 154 7.5.6.2 Management 154 7.5.6.3 Response 154 7.5.7 After Disaster 154 7.5.7.1 Recovery 154 7.5.7.2 Monitoring 154 7.5.7.3 Lessons Learned 155 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 References 158 8 Intelligent Reasoning and Decision‐Making in Disaster Scenarios 163 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 8.4.1 Disaster Prediction 171 8.4.2 Decision-Making in Analyzing the Impact of Disaster 171 8.4.3 Disaster Precautions and Measures 171 8.4.4 Benefits of Decision-Making in Disaster Scenario 172 8.4.5 Technology in Decision-Making Process of a Disaster 173 8.5 AI/Machine Learning in Decision-Making of Disaster Scenario 174 8.5.1 AI/ML in Predisaster Stage 175 8.5.2 AI/ML in During Disaster Stage 176 8.5.3 AI/ML in Postdisaster Stage 178 8.6 AI Methods for Disaster Prediction 179 8.6.1 Cyclone 179 8.6.2 Drought 180 8.6.3 Earthquake 184 8.6.4 Floods 189 8.6.5 Landslides 192 8.7 AI Methods to Analyze the Impact of Disasters 195 8.7.1 Cyclone 196 8.7.2 Drought 198 8.7.3 Earthquake 201 8.7.4 Floods 204 8.7.5 Landslide 205 8.8 AI/ML Methods in Providing Precautionary Measures 210 8.9 Intelligent Reasoning 214 8.10 Conclusion 219 References 220 9 AI Applications in Real-Time Intelligent Automation 229 9.1 Introduction 230 9.2 Related Works 233 9.3 Proposed Methods 235 9.3.1 Use of Drones in Disaster Management 236 9.3.1.1 Understanding Drone Technology 238 9.3.1.2 Components and Functionality 238 9.3.1.3 Types and Classifications 239 9.3.1.4 Applications 239 9.3.1.5 Challenges and Future Trends 239 9.3.1.6 Drone Applications in Earthquake Disaster Response 240 9.3.1.7 Rapid Damage Assessment 240 9.3.1.8 Search and Rescue Operations 240 9.3.1.9 Communication and Coordination 240 9.3.1.10 Environmental Monitoring and Mapping 241 9.3.2 Flood Disaster Management Using the Flood Detection Secure System 241 9.3.2.1 Terminologies in FDSS 243 9.3.2.2 The Process of FDSS 244 9.3.3 Flood Management Using AI and IoT 246 9.3.3.1 Architecture 247 9.4 Conclusion and Future Perspectives 248 References 248 10 Knowledge Management and Processing in Disaster Management 251 10.1 Introduction 252 10.1.1 Importance of Knowledge Management 252 10.1.2 Role of AI 253 10.2 Knowledge Management in Disaster Management 255 10.2.1 Data Collection 255 10.2.2 Information Processing 257 10.2.3 Knowledge Dissemination 259 10.2.4 Decision Support Systems 262 10.3 Integration of AI in Disaster Management 265 10.3.1 Machine Learning Applications 265 10.3.2 Natural Language Processing 265 10.3.3 Predictive Analytics 271 10.4 Challenges and Ethical Considerations 275 10.4.1 Data Privacy 275 10.4.2 Bias and Reliability 278 10.4.3 Resource Allocation 280 10.5 Future Prospects and Innovations 284 10.5.1 Technological Advances 284 10.5.2 Integration with Existing Systems 288 10.5.3 Global Collaboration 290 10.6 Conclusion 294 10.6.1 Summary of Key Points 294 10.6.2 Call to Action 296 10.6.3 Future Vision 298 References 298 11 Perception Technologies for Disaster Situations 301 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 11.4.1 Remote Sensing and Imaging Technologies 306 11.4.2 Computer Vision and Image Analysis 307 11.4.3 Internet of Things (IoT) Sensors 307 11.4.4 Data Fusion and Integration 308 11.4.5 Human-Computer Interaction and Decision Support Systems 309 11.4.6 Ethical and Privacy Considerations 310 11.4.7 Future Directions and Challenges 311 11.5 Conclusion 311 References 312 12 Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective 315 12.1 Introduction 316 12.2 Related Works 318 12.3 Methodology 320 12.4 Experiments and Result Discussion 323 12.5 Conclusion 328 Bibliography 330 13 An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security 333 13.1 Introduction 334 13.2 Evolution of Autonomous Vehicles 335 13.3 Role of AI in Autonomous Systems 336 13.3.1 Architecture Diagram 337 13.3.2 AI Algorithms for Fault Prediction and Recognition 340 13.3.2.1 Isolation Forest Algorithm 341 13.4 Challenges of Artificial Intelligence in Autonomous Systems 344 13.5 Blockchain Security Measures for Autonomous Vehicles 346 13.5.1 Secure Autonomous Vehicle Network Using Blockchain 348 13.6 List of Software/Tools, Design Techniques and Programming Languages for Autonomus Systems 349 13.6.1 Case Studies and Practical Implementations in an Autonomous System 351 13.6.2 Key Findings and Contributions 353 13.7 Conclusion 353 References 354 14 Industrial Experiences in Crop Cultivation Using AI for Disaster Management 357 14.1 Introduction 358 14.1.1 AI in Agriculture 358 14.1.2 Contribution 359 14.2 Related Work 360 14.3 Proposed Framework 362 14.3.1 Construction of Knowledge Graph 363 14.4 Performance Analysis 364 14.4.1 Crop Query Dataset 364 14.4.2 Results Discussion 364 14.5 Conclusion 366 References 366 15 A Comprehensive Review on Robotics in Disaster Response and Recovery 369 15.1 Introduction 370 15.1.1 Role of Robotics in Disaster Response 370 15.1.2 Role of Robotics in Disaster Recovery 371 15.1.3 The Key Objectives of Reviewing Robotics in Disaster Response and Recovery 372 15.2 Disaster Response Robotics 372 15.2.1 Overview of Different Types of Disasters (Natural and Man-Made) 372 15.2.2 Robotics Technologies Used in Disaster Response 374 15.3 Robotics in Disaster Recovery 376 15.3.1 The Transition from the Response to the Recovery Phase in Disaster Management 376 15.3.2 The Role of Robotics in Postdisaster Recovery 377 15.3.3 Infrastructure Inspection and Assessment Using Drones and Ground Robots 379 15.3.4 Debris Clearance and Demolition with Robotic Assistance 380 15.3.5 Rehabilitation and Reconstruction Aided by Robotics in Construction 382 15.3.6 Psychological Support Through Robotic Companionship and Therapy 383 15.3.7 Review of Case Studies or Research Papers Demonstrating the Application and Impact of Robotics in Disaster Recovery Efforts 385 15.4 Future Directions 387 15.4.1 Exploration of Emerging Trends and Future Directions in Disaster Robotics Research 387 15.4.2 Recommendations for Future Research and Development Efforts to Maximize the Potential of Robotics in Disaster Response and Recovery 389 15.5 Conclusion 390 References 391 Index 393
Rajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash
Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.
E. Nirmala, M. Suresh and Sankar Muthu Paramasivam
Rajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar
Geetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya
G. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi
Ravikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar
Sreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.
M. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun li
R. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan
Ganesh Nataraj, K. Mohanasundaram and S. Ramesh Babu
Mithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.
Indra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.
Sagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee
J. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi
Subject Areas: Business & management [KJ]
