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.

• Preface xvii1 Introduction to Sustainable Development and Disaster Management 1Rajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash1.1 Introduction 21.1.1 Overview of Sustainable Development 21.1.1.1 Core Concepts of Sustainable Development 21.1.1.2 Historical Context of Sustainable Development 31.1.1.3 Principles of Sustainable Development 31.1.1.4 Challenges and Opportunities in Achieving Sustainable Development 41.1.2 Importance of Disaster Management 51.1.2.1 Definition and Scope of Disaster Management 51.1.2.2 Phases of Disaster Management 61.1.2.3 Types of Disasters 61.1.2.4 Challenges in Disaster Management 61.1.2.5 Importance of Effective Disaster Management 71.1.2.6 Case Studies of Disaster Management 81.1.3 Intersection of AI, Sustainable Development, and Disaster Management 91.2 Sustainable Development 91.2.1 Definition and Principles 91.2.2 Historical Context and Evolution 91.2.3 Goals and Global Initiatives (SDGs) 101.3 Disaster Management 101.3.1 Definition and Types of Disasters 101.3.2 Phases of Disaster Management 101.3.3 Challenges in Traditional Disaster Management Approaches 111.4 Role of AI in Sustainable Development 121.4.1 AI Technologies and their Applications 121.4.2 Case Studies of AI in Sustainable Development 121.5 Role of AI in Disaster Management 151.5.1 AI Technologies in Disaster Prediction and Early Warning 151.5.2 AI in Disaster Response and Recovery 151.5.3 Case Studies of AI in Disaster Management 161.6 Integration of AI in Sustainable Disaster Management 171.6.1 Benefits of AI Integration 171.6.2 Framework for AI Integration 181.6.2.1 Identifying Key Areas for AI Application 181.6.2.2 Ensuring Data Accessibility and Quality 181.6.2.3 Fostering Collaboration Among Stakeholders 181.6.2.4 Addressing Ethical Considerations 191.6.2.5 Ensuring Transparency 191.6.3 Challenges and Ethical Considerations 191.7 Conclusion 21References 222 Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence– Based Methods 25Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.Introduction to Earthquake Risk Assessment 26Understanding Seismic Hazards 27Data Source of Earthquake Risk Assessment 27Scenario of Earthquake Incidents of the World 28Scenario of Earthquake Incidents of India 29Brief Overview of Earthquake Incidents in India 29Traditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis 33Seismic Hazard Mapping 34Ground Motion Prediction 35Fault Rupture Hazard Analysis 36Site-Specific Studies 37Building Vulnerability Assessment 37Organizations for Earthquake Risk Assessment and Predictions 42Earthquake Risk Assessment Using Artificial Intelligence 43Prediction of Earthquake Using AI 44Algorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms 45Machine Learning Algorithms 45Methods for Earthquake Risk Assessment and Prediction Using AI 46Pattern Recognition in Seismic Data 46Anomaly Detection 47Earthquake Forecasting Model 47Data Fusion and Integration 48Damage and Impact Assessment 49Real-Time Monitoring 50Early Warning Systems 51Risk Mitigation 52Resilience Planning 52Predictive Modeling for Earthquake Forecasting Using AI 54Integration of AI Techniques in Seismic Hazard Analysis 55Construction Practices and Urban Planning for Earthquake Assessment Using AI 56Future Scope of Earthquake Risk Assessment and Prediction Using AI 57Conclusion 58References 593 AI Applications in Earthquake Resistance Using Change in Structural Design 61E. Nirmala, M. Suresh and Sankar Muthu Paramasivam3.1 Introduction 623.2 Review of Literature 633.3 Proposed Techniques 643.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes 643.3.2 Earthquake Prediction Using ANN 673.3.3 AI–Neural Network–Based Earthquake Prediction 673.3.4 AI-Based Dynamic Interpretation Network (DIN)– Multilayer Propagation Algorithm for Earthquake Prediction 683.4 AI- and ML-Based Techniques 703.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms 703.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example 713.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake 733.4.4 Seismic Advancement of Prevailing Masonry Structures 733.5 Conclusion and Future Work 74Bibliography 754 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models 79Rajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar4.1 Introduction 804.2 Related Works 824.3 Dataset Description 834.3.1 Dataset Collection 834.3.2 Dataset Preprocessing 834.4 Methodology 844.4.1 Yolo 844.4.2 YOLOv 3 844.4.3 Tiny-YOLOv 4 854.4.4 YOLOv 5 874.5 Model Evaluation Indicators 884.6 Experimental Results 894.7 Discussion 934.8 Conclusion 94References 955 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives 99Geetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya5.1 Introduction 1005.2 Importance of Intelligent Transportation Systems in Cyclone Resilience 1015.3 Early Warning Systems 1035.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management 1065.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas 1085.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control 1115.7 Conclusion 117References 1176 AI-Enhanced Risk Assessment and Mitigation for Mass Movements 121G. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi6.1 Introduction 1226.2 Understanding Mass Movements 1236.3 Traditional Risk Assessment and Mitigation Methods 1246.4 The Role of AI in Risk Assessment 1256.5 AI-Enhanced Mitigation Strategies 1276.6 Challenges and Ethical Considerations 1296.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management 1306.8 Case Studies in AI-Enhanced Mass Movement Management 1326.9 Conclusions 134References 1357 Distributed AI Systems for Disaster Response and Recovery 137Ravikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar7.1 Introduction 1387.2 Technology Applied in Critical Cases 1417.2.1 Disaster Management Architecture 1437.2.2 Proposed Framework 1447.2.3 Disaster Management Ontology 1457.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology 1457.4 ml and Deep Learning Methods: An Overview 1467.4.1 Convolutional Neural Network 1477.4.2 Lstm 1487.4.3 Support Vector Machine 1487.4.4 ML/DL Methods for Disaster and Hazard Prediction 1487.4.5 ML/DL Methods for Risk and Vulnerability Assessment 1497.4.6 ML/DL Methods for Disaster Detection 1507.4.7 ML/DL Methods for Disaster Monitoring 1507.4.8 ML/DL Methods for Damage Assessment 1507.5 Phases of Disaster Management 1517.5.1 Prediction 1517.5.2 Detection 1527.5.3 Response 1527.5.4 Recovery 1527.5.5 Before Disaster 1527.5.5.1 Risk Assessment 1527.5.5.2 Mitigation 1537.5.5.3 Prevention 1537.5.5.4 Prediction 1537.5.5.5 Detection 1537.5.6 During Disaster 1537.5.6.1 Preparation 1547.5.6.2 Management 1547.5.6.3 Response 1547.5.7 After Disaster 1547.5.7.1 Recovery 1547.5.7.2 Monitoring 1547.5.7.3 Lessons Learned 1557.6 Disaster Management and Disaster Resilience 1557.7 Applications of AI for Disaster Management 1567.8 AI Applications in Disaster Mitigation 1567.9 Conclusion 157References 1588 Intelligent Reasoning and Decision‐Making in Disaster Scenarios 163Sreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.8.1 Introduction 1648.2 Types of Natural Disasters 1658.3 Impact of Natural Disasters 1678.4 Decision-Making in a Disaster Scenario 1708.4.1 Disaster Prediction 1718.4.2 Decision-Making in Analyzing the Impact of Disaster 1718.4.3 Disaster Precautions and Measures 1718.4.4 Benefits of Decision-Making in Disaster Scenario 1728.4.5 Technology in Decision-Making Process of a Disaster 1738.5 AI/Machine Learning in Decision-Making of Disaster Scenario 1748.5.1 AI/ML in Predisaster Stage 1758.5.2 AI/ML in During Disaster Stage 1768.5.3 AI/ML in Postdisaster Stage 1788.6 AI Methods for Disaster Prediction 1798.6.1 Cyclone 1798.6.2 Drought 1808.6.3 Earthquake 1848.6.4 Floods 1898.6.5 Landslides 1928.7 AI Methods to Analyze the Impact of Disasters 1958.7.1 Cyclone 1968.7.2 Drought 1988.7.3 Earthquake 2018.7.4 Floods 2048.7.5 Landslide 2058.8 AI/ML Methods in Providing Precautionary Measures 2108.9 Intelligent Reasoning 2148.10 Conclusion 219References 2209 AI Applications in Real-Time Intelligent Automation 229M. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun li9.1 Introduction 2309.2 Related Works 2339.3 Proposed Methods 2359.3.1 Use of Drones in Disaster Management 2369.3.1.1 Understanding Drone Technology 2389.3.1.2 Components and Functionality 2389.3.1.3 Types and Classifications 2399.3.1.4 Applications 2399.3.1.5 Challenges and Future Trends 2399.3.1.6 Drone Applications in Earthquake Disaster Response 2409.3.1.7 Rapid Damage Assessment 2409.3.1.8 Search and Rescue Operations 2409.3.1.9 Communication and Coordination 2409.3.1.10 Environmental Monitoring and Mapping 2419.3.2 Flood Disaster Management Using the Flood Detection Secure System 2419.3.2.1 Terminologies in FDSS 2439.3.2.2 The Process of FDSS 2449.3.3 Flood Management Using AI and IoT 2469.3.3.1 Architecture 2479.4 Conclusion and Future Perspectives 248References 24810 Knowledge Management and Processing in Disaster Management 251R. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan10.1 Introduction 25210.1.1 Importance of Knowledge Management 25210.1.2 Role of AI 25310.2 Knowledge Management in Disaster Management 25510.2.1 Data Collection 25510.2.2 Information Processing 25710.2.3 Knowledge Dissemination 25910.2.4 Decision Support Systems 26210.3 Integration of AI in Disaster Management 26510.3.1 Machine Learning Applications 26510.3.2 Natural Language Processing 26510.3.3 Predictive Analytics 27110.4 Challenges and Ethical Considerations 27510.4.1 Data Privacy 27510.4.2 Bias and Reliability 27810.4.3 Resource Allocation 28010.5 Future Prospects and Innovations 28410.5.1 Technological Advances 28410.5.2 Integration with Existing Systems 28810.5.3 Global Collaboration 29010.6 Conclusion 29410.6.1 Summary of Key Points 29410.6.2 Call to Action 29610.6.3 Future Vision 298References 29811 Perception Technologies for Disaster Situations 301Ganesh Nataraj, K. Mohanasundaram and S. Ramesh Babu11.1 Introduction 30211.2 Understanding Disaster Situations 30311.3 Role of Perception Technologies 30511.4 Categories of Perception Disaster Technologies 30611.4.1 Remote Sensing and Imaging Technologies 30611.4.2 Computer Vision and Image Analysis 30711.4.3 Internet of Things (IoT) Sensors 30711.4.4 Data Fusion and Integration 30811.4.5 Human-Computer Interaction and Decision Support Systems 30911.4.6 Ethical and Privacy Considerations 31011.4.7 Future Directions and Challenges 31111.5 Conclusion 311References 31212 Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective 315Mithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.12.1 Introduction 31612.2 Related Works 31812.3 Methodology 32012.4 Experiments and Result Discussion 32312.5 Conclusion 328Bibliography 33013 An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security 333Indra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.13.1 Introduction 33413.2 Evolution of Autonomous Vehicles 33513.3 Role of AI in Autonomous Systems 33613.3.1 Architecture Diagram 33713.3.2 AI Algorithms for Fault Prediction and Recognition 34013.3.2.1 Isolation Forest Algorithm 34113.4 Challenges of Artificial Intelligence in Autonomous Systems 34413.5 Blockchain Security Measures for Autonomous Vehicles 34613.5.1 Secure Autonomous Vehicle Network Using Blockchain 34813.6 List of Software/Tools, Design Techniques and Programming Languages for Autonomus Systems 34913.6.1 Case Studies and Practical Implementations in an Autonomous System 35113.6.2 Key Findings and Contributions 35313.7 Conclusion 353References 35414 Industrial Experiences in Crop Cultivation Using AI for Disaster Management 357Sagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee14.1 Introduction 35814.1.1 AI in Agriculture 35814.1.2 Contribution 35914.2 Related Work 36014.3 Proposed Framework 36214.3.1 Construction of Knowledge Graph 36314.4 Performance Analysis 36414.4.1 Crop Query Dataset 36414.4.2 Results Discussion 36414.5 Conclusion 366References 36615 A Comprehensive Review on Robotics in Disaster Response and Recovery 369J. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi15.1 Introduction 37015.1.1 Role of Robotics in Disaster Response 37015.1.2 Role of Robotics in Disaster Recovery 37115.1.3 The Key Objectives of Reviewing Robotics in Disaster Response and Recovery 37215.2 Disaster Response Robotics 37215.2.1 Overview of Different Types of Disasters (Natural and Man-Made) 37215.2.2 Robotics Technologies Used in Disaster Response 37415.3 Robotics in Disaster Recovery 37615.3.1 The Transition from the Response to the Recovery Phase in Disaster Management 37615.3.2 The Role of Robotics in Postdisaster Recovery 37715.3.3 Infrastructure Inspection and Assessment Using Drones and Ground Robots 37915.3.4 Debris Clearance and Demolition with Robotic Assistance 38015.3.5 Rehabilitation and Reconstruction Aided by Robotics in Construction 38215.3.6 Psychological Support Through Robotic Companionship and Therapy 38315.3.7 Review of Case Studies or Research Papers Demonstrating the Application and Impact of Robotics in Disaster Recovery Efforts 38515.4 Future Directions 38715.4.1 Exploration of Emerging Trends and Future Directions in Disaster Robotics Research 38715.4.2 Recommendations for Future Research and Development Efforts to Maximize the Potential of Robotics in Disaster Response and Recovery 38915.5 Conclusion 390References 391Index 393

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.

• Preface xvii1 Introduction to Sustainable Development and Disaster Management 1Rajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash1.1 Introduction 21.1.1 Overview of Sustainable Development 21.1.1.1 Core Concepts of Sustainable Development 21.1.1.2 Historical Context of Sustainable Development 31.1.1.3 Principles of Sustainable Development 31.1.1.4 Challenges and Opportunities in Achieving Sustainable Development 41.1.2 Importance of Disaster Management 51.1.2.1 Definition and Scope of Disaster Management 51.1.2.2 Phases of Disaster Management 61.1.2.3 Types of Disasters 61.1.2.4 Challenges in Disaster Management 61.1.2.5 Importance of Effective Disaster Management 71.1.2.6 Case Studies of Disaster Management 81.1.3 Intersection of AI, Sustainable Development, and Disaster Management 91.2 Sustainable Development 91.2.1 Definition and Principles 91.2.2 Historical Context and Evolution 91.2.3 Goals and Global Initiatives (SDGs) 101.3 Disaster Management 101.3.1 Definition and Types of Disasters 101.3.2 Phases of Disaster Management 101.3.3 Challenges in Traditional Disaster Management Approaches 111.4 Role of AI in Sustainable Development 121.4.1 AI Technologies and their Applications 121.4.2 Case Studies of AI in Sustainable Development 121.5 Role of AI in Disaster Management 151.5.1 AI Technologies in Disaster Prediction and Early Warning 151.5.2 AI in Disaster Response and Recovery 151.5.3 Case Studies of AI in Disaster Management 161.6 Integration of AI in Sustainable Disaster Management 171.6.1 Benefits of AI Integration 171.6.2 Framework for AI Integration 181.6.2.1 Identifying Key Areas for AI Application 181.6.2.2 Ensuring Data Accessibility and Quality 181.6.2.3 Fostering Collaboration Among Stakeholders 181.6.2.4 Addressing Ethical Considerations 191.6.2.5 Ensuring Transparency 191.6.3 Challenges and Ethical Considerations 191.7 Conclusion 21References 222 Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence– Based Methods 25Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.Introduction to Earthquake Risk Assessment 26Understanding Seismic Hazards 27Data Source of Earthquake Risk Assessment 27Scenario of Earthquake Incidents of the World 28Scenario of Earthquake Incidents of India 29Brief Overview of Earthquake Incidents in India 29Traditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis 33Seismic Hazard Mapping 34Ground Motion Prediction 35Fault Rupture Hazard Analysis 36Site-Specific Studies 37Building Vulnerability Assessment 37Organizations for Earthquake Risk Assessment and Predictions 42Earthquake Risk Assessment Using Artificial Intelligence 43Prediction of Earthquake Using AI 44Algorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms 45Machine Learning Algorithms 45Methods for Earthquake Risk Assessment and Prediction Using AI 46Pattern Recognition in Seismic Data 46Anomaly Detection 47Earthquake Forecasting Model 47Data Fusion and Integration 48Damage and Impact Assessment 49Real-Time Monitoring 50Early Warning Systems 51Risk Mitigation 52Resilience Planning 52Predictive Modeling for Earthquake Forecasting Using AI 54Integration of AI Techniques in Seismic Hazard Analysis 55Construction Practices and Urban Planning for Earthquake Assessment Using AI 56Future Scope of Earthquake Risk Assessment and Prediction Using AI 57Conclusion 58References 593 AI Applications in Earthquake Resistance Using Change in Structural Design 61E. Nirmala, M. Suresh and Sankar Muthu Paramasivam3.1 Introduction 623.2 Review of Literature 633.3 Proposed Techniques 643.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes 643.3.2 Earthquake Prediction Using ANN 673.3.3 AI–Neural Network–Based Earthquake Prediction 673.3.4 AI-Based Dynamic Interpretation Network (DIN)– Multilayer Propagation Algorithm for Earthquake Prediction 683.4 AI- and ML-Based Techniques 703.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms 703.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example 713.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake 733.4.4 Seismic Advancement of Prevailing Masonry Structures 733.5 Conclusion and Future Work 74Bibliography 754 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models 79Rajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar4.1 Introduction 804.2 Related Works 824.3 Dataset Description 834.3.1 Dataset Collection 834.3.2 Dataset Preprocessing 834.4 Methodology 844.4.1 Yolo 844.4.2 YOLOv 3 844.4.3 Tiny-YOLOv 4 854.4.4 YOLOv 5 874.5 Model Evaluation Indicators 884.6 Experimental Results 894.7 Discussion 934.8 Conclusion 94References 955 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives 99Geetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya5.1 Introduction 1005.2 Importance of Intelligent Transportation Systems in Cyclone Resilience 1015.3 Early Warning Systems 1035.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management 1065.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas 1085.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control 1115.7 Conclusion 117References 1176 AI-Enhanced Risk Assessment and Mitigation for Mass Movements 121G. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi6.1 Introduction 1226.2 Understanding Mass Movements 1236.3 Traditional Risk Assessment and Mitigation Methods 1246.4 The Role of AI in Risk Assessment 1256.5 AI-Enhanced Mitigation Strategies 1276.6 Challenges and Ethical Considerations 1296.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management 1306.8 Case Studies in AI-Enhanced Mass Movement Management 1326.9 Conclusions 134References 1357 Distributed AI Systems for Disaster Response and Recovery 137Ravikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar7.1 Introduction 1387.2 Technology Applied in Critical Cases 1417.2.1 Disaster Management Architecture 1437.2.2 Proposed Framework 1447.2.3 Disaster Management Ontology 1457.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology 1457.4 ml and Deep Learning Methods: An Overview 1467.4.1 Convolutional Neural Network 1477.4.2 Lstm 1487.4.3 Support Vector Machine 1487.4.4 ML/DL Methods for Disaster and Hazard Prediction 1487.4.5 ML/DL Methods for Risk and Vulnerability Assessment 1497.4.6 ML/DL Methods for Disaster Detection 1507.4.7 ML/DL Methods for Disaster Monitoring 1507.4.8 ML/DL Methods for Damage Assessment 1507.5 Phases of Disaster Management 1517.5.1 Prediction 1517.5.2 Detection 1527.5.3 Response 1527.5.4 Recovery 1527.5.5 Before Disaster 1527.5.5.1 Risk Assessment 1527.5.5.2 Mitigation 1537.5.5.3 Prevention 1537.5.5.4 Prediction 1537.5.5.5 Detection 1537.5.6 During Disaster 1537.5.6.1 Preparation 1547.5.6.2 Management 1547.5.6.3 Response 1547.5.7 After Disaster 1547.5.7.1 Recovery 1547.5.7.2 Monitoring 1547.5.7.3 Lessons Learned 1557.6 Disaster Management and Disaster Resilience 1557.7 Applications of AI for Disaster Management 1567.8 AI Applications in Disaster Mitigation 1567.9 Conclusion 157References 1588 Intelligent Reasoning and Decision‐Making in Disaster Scenarios 163Sreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.8.1 Introduction 1648.2 Types of Natural Disasters 1658.3 Impact of Natural Disasters 1678.4 Decision-Making in a Disaster Scenario 1708.4.1 Disaster Prediction 1718.4.2 Decision-Making in Analyzing the Impact of Disaster 1718.4.3 Disaster Precautions and Measures 1718.4.4 Benefits of Decision-Making in Disaster Scenario 1728.4.5 Technology in Decision-Making Process of a Disaster 1738.5 AI/Machine Learning in Decision-Making of Disaster Scenario 1748.5.1 AI/ML in Predisaster Stage 1758.5.2 AI/ML in During Disaster Stage 1768.5.3 AI/ML in Postdisaster Stage 1788.6 AI Methods for Disaster Prediction 1798.6.1 Cyclone 1798.6.2 Drought 1808.6.3 Earthquake 1848.6.4 Floods 1898.6.5 Landslides 1928.7 AI Methods to Analyze the Impact of Disasters 1958.7.1 Cyclone 1968.7.2 Drought 1988.7.3 Earthquake 2018.7.4 Floods 2048.7.5 Landslide 2058.8 AI/ML Methods in Providing Precautionary Measures 2108.9 Intelligent Reasoning 2148.10 Conclusion 219References 2209 AI Applications in Real-Time Intelligent Automation 229M. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun li9.1 Introduction 2309.2 Related Works 2339.3 Proposed Methods 2359.3.1 Use of Drones in Disaster Management 2369.3.1.1 Understanding Drone Technology 2389.3.1.2 Components and Functionality 2389.3.1.3 Types and Classifications 2399.3.1.4 Applications 2399.3.1.5 Challenges and Future Trends 2399.3.1.6 Drone Applications in Earthquake Disaster Response 2409.3.1.7 Rapid Damage Assessment 2409.3.1.8 Search and Rescue Operations 2409.3.1.9 Communication and Coordination 2409.3.1.10 Environmental Monitoring and Mapping 2419.3.2 Flood Disaster Management Using the Flood Detection Secure System 2419.3.2.1 Terminologies in FDSS 2439.3.2.2 The Process of FDSS 2449.3.3 Flood Management Using AI and IoT 2469.3.3.1 Architecture 2479.4 Conclusion and Future Perspectives 248References 24810 Knowledge Management and Processing in Disaster Management 251R. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan10.1 Introduction 25210.1.1 Importance of Knowledge Management 25210.1.2 Role of AI 25310.2 Knowledge Management in Disaster Management 25510.2.1 Data Collection 25510.2.2 Information Processing 25710.2.3 Knowledge Dissemination 25910.2.4 Decision Support Systems 26210.3 Integration of AI in Disaster Management 26510.3.1 Machine Learning Applications 26510.3.2 Natural Language Processing 26510.3.3 Predictive Analytics 27110.4 Challenges and Ethical Considerations 27510.4.1 Data Privacy 27510.4.2 Bias and Reliability 27810.4.3 Resource Allocation 28010.5 Future Prospects and Innovations 28410.5.1 Technological Advances 28410.5.2 Integration with Existing Systems 28810.5.3 Global Collaboration 29010.6 Conclusion 29410.6.1 Summary of Key Points 29410.6.2 Call to Action 29610.6.3 Future Vision 298References 29811 Perception Technologies for Disaster Situations 301Ganesh Nataraj, K. Mohanasundaram and S. Ramesh Babu11.1 Introduction 30211.2 Understanding Disaster Situations 30311.3 Role of Perception Technologies 30511.4 Categories of Perception Disaster Technologies 30611.4.1 Remote Sensing and Imaging Technologies 30611.4.2 Computer Vision and Image Analysis 30711.4.3 Internet of Things (IoT) Sensors 30711.4.4 Data Fusion and Integration 30811.4.5 Human-Computer Interaction and Decision Support Systems 30911.4.6 Ethical and Privacy Considerations 31011.4.7 Future Directions and Challenges 31111.5 Conclusion 311References 31212 Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective 315Mithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.12.1 Introduction 31612.2 Related Works 31812.3 Methodology 32012.4 Experiments and Result Discussion 32312.5 Conclusion 328Bibliography 33013 An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security 333Indra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.13.1 Introduction 33413.2 Evolution of Autonomous Vehicles 33513.3 Role of AI in Autonomous Systems 33613.3.1 Architecture Diagram 33713.3.2 AI Algorithms for Fault Prediction and Recognition 34013.3.2.1 Isolation Forest Algorithm 34113.4 Challenges of Artificial Intelligence in Autonomous Systems 34413.5 Blockchain Security Measures for Autonomous Vehicles 34613.5.1 Secure Autonomous Vehicle Network Using Blockchain 34813.6 List of Software/Tools, Design Techniques and Programming Languages for Autonomus Systems 34913.6.1 Case Studies and Practical Implementations in an Autonomous System 35113.6.2 Key Findings and Contributions 35313.7 Conclusion 353References 35414 Industrial Experiences in Crop Cultivation Using AI for Disaster Management 357Sagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee14.1 Introduction 35814.1.1 AI in Agriculture 35814.1.2 Contribution 35914.2 Related Work 36014.3 Proposed Framework 36214.3.1 Construction of Knowledge Graph 36314.4 Performance Analysis 36414.4.1 Crop Query Dataset 36414.4.2 Results Discussion 36414.5 Conclusion 366References 36615 A Comprehensive Review on Robotics in Disaster Response and Recovery 369J. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi15.1 Introduction 37015.1.1 Role of Robotics in Disaster Response 37015.1.2 Role of Robotics in Disaster Recovery 37115.1.3 The Key Objectives of Reviewing Robotics in Disaster Response and Recovery 37215.2 Disaster Response Robotics 37215.2.1 Overview of Different Types of Disasters (Natural and Man-Made) 37215.2.2 Robotics Technologies Used in Disaster Response 37415.3 Robotics in Disaster Recovery 37615.3.1 The Transition from the Response to the Recovery Phase in Disaster Management 37615.3.2 The Role of Robotics in Postdisaster Recovery 37715.3.3 Infrastructure Inspection and Assessment Using Drones and Ground Robots 37915.3.4 Debris Clearance and Demolition with Robotic Assistance 38015.3.5 Rehabilitation and Reconstruction Aided by Robotics in Construction 38215.3.6 Psychological Support Through Robotic Companionship and Therapy 38315.3.7 Review of Case Studies or Research Papers Demonstrating the Application and Impact of Robotics in Disaster Recovery Efforts 38515.4 Future Directions 38715.4.1 Exploration of Emerging Trends and Future Directions in Disaster Robotics Research 38715.4.2 Recommendations for Future Research and Development Efforts to Maximize the Potential of Robotics in Disaster Response and Recovery 38915.5 Conclusion 390References 391Index 393

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.

• Preface xvii1 Introduction to Sustainable Development and Disaster Management 1Rajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash1.1 Introduction 21.1.1 Overview of Sustainable Development 21.1.1.1 Core Concepts of Sustainable Development 21.1.1.2 Historical Context of Sustainable Development 31.1.1.3 Principles of Sustainable Development 31.1.1.4 Challenges and Opportunities in Achieving Sustainable Development 41.1.2 Importance of Disaster Management 51.1.2.1 Definition and Scope of Disaster Management 51.1.2.2 Phases of Disaster Management 61.1.2.3 Types of Disasters 61.1.2.4 Challenges in Disaster Management 61.1.2.5 Importance of Effective Disaster Management 71.1.2.6 Case Studies of Disaster Management 81.1.3 Intersection of AI, Sustainable Development, and Disaster Management 91.2 Sustainable Development 91.2.1 Definition and Principles 91.2.2 Historical Context and Evolution 91.2.3 Goals and Global Initiatives (SDGs) 101.3 Disaster Management 101.3.1 Definition and Types of Disasters 101.3.2 Phases of Disaster Management 101.3.3 Challenges in Traditional Disaster Management Approaches 111.4 Role of AI in Sustainable Development 121.4.1 AI Technologies and their Applications 121.4.2 Case Studies of AI in Sustainable Development 121.5 Role of AI in Disaster Management 151.5.1 AI Technologies in Disaster Prediction and Early Warning 151.5.2 AI in Disaster Response and Recovery 151.5.3 Case Studies of AI in Disaster Management 161.6 Integration of AI in Sustainable Disaster Management 171.6.1 Benefits of AI Integration 171.6.2 Framework for AI Integration 181.6.2.1 Identifying Key Areas for AI Application 181.6.2.2 Ensuring Data Accessibility and Quality 181.6.2.3 Fostering Collaboration Among Stakeholders 181.6.2.4 Addressing Ethical Considerations 191.6.2.5 Ensuring Transparency 191.6.3 Challenges and Ethical Considerations 191.7 Conclusion 21References 222 Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence– Based Methods 25Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.Introduction to Earthquake Risk Assessment 26Understanding Seismic Hazards 27Data Source of Earthquake Risk Assessment 27Scenario of Earthquake Incidents of the World 28Scenario of Earthquake Incidents of India 29Brief Overview of Earthquake Incidents in India 29Traditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis 33Seismic Hazard Mapping 34Ground Motion Prediction 35Fault Rupture Hazard Analysis 36Site-Specific Studies 37Building Vulnerability Assessment 37Organizations for Earthquake Risk Assessment and Predictions 42Earthquake Risk Assessment Using Artificial Intelligence 43Prediction of Earthquake Using AI 44Algorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms 45Machine Learning Algorithms 45Methods for Earthquake Risk Assessment and Prediction Using AI 46Pattern Recognition in Seismic Data 46Anomaly Detection 47Earthquake Forecasting Model 47Data Fusion and Integration 48Damage and Impact Assessment 49Real-Time Monitoring 50Early Warning Systems 51Risk Mitigation 52Resilience Planning 52Predictive Modeling for Earthquake Forecasting Using AI 54Integration of AI Techniques in Seismic Hazard Analysis 55Construction Practices and Urban Planning for Earthquake Assessment Using AI 56Future Scope of Earthquake Risk Assessment and Prediction Using AI 57Conclusion 58References 593 AI Applications in Earthquake Resistance Using Change in Structural Design 61E. Nirmala, M. Suresh and Sankar Muthu Paramasivam3.1 Introduction 623.2 Review of Literature 633.3 Proposed Techniques 643.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes 643.3.2 Earthquake Prediction Using ANN 673.3.3 AI–Neural Network–Based Earthquake Prediction 673.3.4 AI-Based Dynamic Interpretation Network (DIN)– Multilayer Propagation Algorithm for Earthquake Prediction 683.4 AI- and ML-Based Techniques 703.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms 703.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example 713.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake 733.4.4 Seismic Advancement of Prevailing Masonry Structures 733.5 Conclusion and Future Work 74Bibliography 754 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models 79Rajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar4.1 Introduction 804.2 Related Works 824.3 Dataset Description 834.3.1 Dataset Collection 834.3.2 Dataset Preprocessing 834.4 Methodology 844.4.1 Yolo 844.4.2 YOLOv 3 844.4.3 Tiny-YOLOv 4 854.4.4 YOLOv 5 874.5 Model Evaluation Indicators 884.6 Experimental Results 894.7 Discussion 934.8 Conclusion 94References 955 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives 99Geetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya5.1 Introduction 1005.2 Importance of Intelligent Transportation Systems in Cyclone Resilience 1015.3 Early Warning Systems 1035.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management 1065.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas 1085.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control 1115.7 Conclusion 117References 1176 AI-Enhanced Risk Assessment and Mitigation for Mass Movements 121G. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi6.1 Introduction 1226.2 Understanding Mass Movements 1236.3 Traditional Risk Assessment and Mitigation Methods 1246.4 The Role of AI in Risk Assessment 1256.5 AI-Enhanced Mitigation Strategies 1276.6 Challenges and Ethical Considerations 1296.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management 1306.8 Case Studies in AI-Enhanced Mass Movement Management 1326.9 Conclusions 134References 1357 Distributed AI Systems for Disaster Response and Recovery 137Ravikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar7.1 Introduction 1387.2 Technology Applied in Critical Cases 1417.2.1 Disaster Management Architecture 1437.2.2 Proposed Framework 1447.2.3 Disaster Management Ontology 1457.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology 1457.4 ml and Deep Learning Methods: An Overview 1467.4.1 Convolutional Neural Network 1477.4.2 Lstm 1487.4.3 Support Vector Machine 1487.4.4 ML/DL Methods for Disaster and Hazard Prediction 1487.4.5 ML/DL Methods for Risk and Vulnerability Assessment 1497.4.6 ML/DL Methods for Disaster Detection 1507.4.7 ML/DL Methods for Disaster Monitoring 1507.4.8 ML/DL Methods for Damage Assessment 1507.5 Phases of Disaster Management 1517.5.1 Prediction 1517.5.2 Detection 1527.5.3 Response 1527.5.4 Recovery 1527.5.5 Before Disaster 1527.5.5.1 Risk Assessment 1527.5.5.2 Mitigation 1537.5.5.3 Prevention 1537.5.5.4 Prediction 1537.5.5.5 Detection 1537.5.6 During Disaster 1537.5.6.1 Preparation 1547.5.6.2 Management 1547.5.6.3 Response 1547.5.7 After Disaster 1547.5.7.1 Recovery 1547.5.7.2 Monitoring 1547.5.7.3 Lessons Learned 1557.6 Disaster Management and Disaster Resilience 1557.7 Applications of AI for Disaster Management 1567.8 AI Applications in Disaster Mitigation 1567.9 Conclusion 157References 1588 Intelligent Reasoning and Decision‐Making in Disaster Scenarios 163Sreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.8.1 Introduction 1648.2 Types of Natural Disasters 1658.3 Impact of Natural Disasters 1678.4 Decision-Making in a Disaster Scenario 1708.4.1 Disaster Prediction 1718.4.2 Decision-Making in Analyzing the Impact of Disaster 1718.4.3 Disaster Precautions and Measures 1718.4.4 Benefits of Decision-Making in Disaster Scenario 1728.4.5 Technology in Decision-Making Process of a Disaster 1738.5 AI/Machine Learning in Decision-Making of Disaster Scenario 1748.5.1 AI/ML in Predisaster Stage 1758.5.2 AI/ML in During Disaster Stage 1768.5.3 AI/ML in Postdisaster Stage 1788.6 AI Methods for Disaster Prediction 1798.6.1 Cyclone 1798.6.2 Drought 1808.6.3 Earthquake 1848.6.4 Floods 1898.6.5 Landslides 1928.7 AI Methods to Analyze the Impact of Disasters 1958.7.1 Cyclone 1968.7.2 Drought 1988.7.3 Earthquake 2018.7.4 Floods 2048.7.5 Landslide 2058.8 AI/ML Methods in Providing Precautionary Measures 2108.9 Intelligent Reasoning 2148.10 Conclusion 219References 2209 AI Applications in Real-Time Intelligent Automation 229M. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun li9.1 Introduction 2309.2 Related Works 2339.3 Proposed Methods 2359.3.1 Use of Drones in Disaster Management 2369.3.1.1 Understanding Drone Technology 2389.3.1.2 Components and Functionality 2389.3.1.3 Types and Classifications 2399.3.1.4 Applications 2399.3.1.5 Challenges and Future Trends 2399.3.1.6 Drone Applications in Earthquake Disaster Response 2409.3.1.7 Rapid Damage Assessment 2409.3.1.8 Search and Rescue Operations 2409.3.1.9 Communication and Coordination 2409.3.1.10 Environmental Monitoring and Mapping 2419.3.2 Flood Disaster Management Using the Flood Detection Secure System 2419.3.2.1 Terminologies in FDSS 2439.3.2.2 The Process of FDSS 2449.3.3 Flood Management Using AI and IoT 2469.3.3.1 Architecture 2479.4 Conclusion and Future Perspectives 248References 24810 Knowledge Management and Processing in Disaster Management 251R. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan10.1 Introduction 25210.1.1 Importance of Knowledge Management 25210.1.2 Role of AI 25310.2 Knowledge Management in Disaster Management 25510.2.1 Data Collection 25510.2.2 Information Processing 25710.2.3 Knowledge Dissemination 25910.2.4 Decision Support Systems 26210.3 Integration of AI in Disaster Management 26510.3.1 Machine Learning Applications 26510.3.2 Natural Language Processing 26510.3.3 Predictive Analytics 27110.4 Challenges and Ethical Considerations 27510.4.1 Data Privacy 27510.4.2 Bias and Reliability 27810.4.3 Resource Allocation 28010.5 Future Prospects and Innovations 28410.5.1 Technological Advances 28410.5.2 Integration with Existing Systems 28810.5.3 Global Collaboration 29010.6 Conclusion 29410.6.1 Summary of Key Points 29410.6.2 Call to Action 29610.6.3 Future Vision 298References 29811 Perception Technologies for Disaster Situations 301Ganesh Nataraj, K. Mohanasundaram and S. Ramesh Babu11.1 Introduction 30211.2 Understanding Disaster Situations 30311.3 Role of Perception Technologies 30511.4 Categories of Perception Disaster Technologies 30611.4.1 Remote Sensing and Imaging Technologies 30611.4.2 Computer Vision and Image Analysis 30711.4.3 Internet of Things (IoT) Sensors 30711.4.4 Data Fusion and Integration 30811.4.5 Human-Computer Interaction and Decision Support Systems 30911.4.6 Ethical and Privacy Considerations 31011.4.7 Future Directions and Challenges 31111.5 Conclusion 311References 31212 Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective 315Mithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.12.1 Introduction 31612.2 Related Works 31812.3 Methodology 32012.4 Experiments and Result Discussion 32312.5 Conclusion 328Bibliography 33013 An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security 333Indra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.13.1 Introduction 33413.2 Evolution of Autonomous Vehicles 33513.3 Role of AI in Autonomous Systems 33613.3.1 Architecture Diagram 33713.3.2 AI Algorithms for Fault Prediction and Recognition 34013.3.2.1 Isolation Forest Algorithm 34113.4 Challenges of Artificial Intelligence in Autonomous Systems 34413.5 Blockchain Security Measures for Autonomous Vehicles 34613.5.1 Secure Autonomous Vehicle Network Using Blockchain 34813.6 List of Software/Tools, Design Techniques and Programming Languages for Autonomus Systems 34913.6.1 Case Studies and Practical Implementations in an Autonomous System 35113.6.2 Key Findings and Contributions 35313.7 Conclusion 353References 35414 Industrial Experiences in Crop Cultivation Using AI for Disaster Management 357Sagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee14.1 Introduction 35814.1.1 AI in Agriculture 35814.1.2 Contribution 35914.2 Related Work 36014.3 Proposed Framework 36214.3.1 Construction of Knowledge Graph 36314.4 Performance Analysis 36414.4.1 Crop Query Dataset 36414.4.2 Results Discussion 36414.5 Conclusion 366References 36615 A Comprehensive Review on Robotics in Disaster Response and Recovery 369J. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi15.1 Introduction 37015.1.1 Role of Robotics in Disaster Response 37015.1.2 Role of Robotics in Disaster Recovery 37115.1.3 The Key Objectives of Reviewing Robotics in Disaster Response and Recovery 37215.2 Disaster Response Robotics 37215.2.1 Overview of Different Types of Disasters (Natural and Man-Made) 37215.2.2 Robotics Technologies Used in Disaster Response 37415.3 Robotics in Disaster Recovery 37615.3.1 The Transition from the Response to the Recovery Phase in Disaster Management 37615.3.2 The Role of Robotics in Postdisaster Recovery 37715.3.3 Infrastructure Inspection and Assessment Using Drones and Ground Robots 37915.3.4 Debris Clearance and Demolition with Robotic Assistance 38015.3.5 Rehabilitation and Reconstruction Aided by Robotics in Construction 38215.3.6 Psychological Support Through Robotic Companionship and Therapy 38315.3.7 Review of Case Studies or Research Papers Demonstrating the Application and Impact of Robotics in Disaster Recovery Efforts 38515.4 Future Directions 38715.4.1 Exploration of Emerging Trends and Future Directions in Disaster Robotics Research 38715.4.2 Recommendations for Future Research and Development Efforts to Maximize the Potential of Robotics in Disaster Response and Recovery 38915.5 Conclusion 390References 391Index 393