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 1.1.1 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 Jeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.

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 E. Nirmala, M. Suresh and Sankar Muthu Paramasivam 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 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 4.3.1 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 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 References 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 References 135 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 7.2.1 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 DecisionMaking 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 8.4.1 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.