{"product_id":"integrating-ai-for-sustainable-disaster-management-building-resilience-and-preventing-catastrophes-hardback-9781394271573","title":"Integrating AI for Sustainable Disaster Management; Building Resilience and Preventing Catastrophes (Hardback) 9781394271573","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntegrating AI for Sustainable Disaster Management\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eBuilding Resilience and Preventing Catastrophes\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePalanichamy Naveen (Edited by), Naveen (Author), R. Maheswar (Edited by), K. Mohanasundaram (Edited by), Rajasekaran Thangaraj (Edited by), S. Arivazhagan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394271573, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e28 x 19 x 2.6 cm, 0.666 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eFuture-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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eBy 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. \u003c\/p\u003e\n\u003cp\u003eReaders will find the volume: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplores how artificial intelligence enhances risk assessment, early warning systems, and realtime disaster response;\u003c\/li\u003e \u003cli\u003eProvides practical insights through detailed examples of AI applications in earthquakes, cyclones, and mass movement management;\u003c\/li\u003e \u003cli\u003eDemonstrates how AI can support sustainable practices and align with global development goals to build resilient communities;\u003c\/li\u003e \u003cli\u003eProvides comprehensive coverage, combining expertise from AI, engineering, sustainability development, and disaster management practitioners;\u003c\/li\u003e \u003cli\u003eIntroduces the latest AI techniques, including IoT, big data, deep learning, and robotics, for effective disaster prevention and recovery.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers, civil, structural, and environmental engineers, policymakers, and graduate students involved in disaster management, sustainable development, AI, and data science.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Sustainable Development and Disaster Management 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajasekaran Thangaraj, Palanichamy Naveen, Maheswar R., Mohanasundaram K., Arivazhagan S. and Kolla Bhanu Prakash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.1.1 Overview of Sustainable Development 2\u003c\/p\u003e \u003cp\u003e1.1.1.1 Core Concepts of Sustainable Development 2\u003c\/p\u003e \u003cp\u003e1.1.1.2 Historical Context of Sustainable Development 3\u003c\/p\u003e \u003cp\u003e1.1.1.3 Principles of Sustainable Development 3\u003c\/p\u003e \u003cp\u003e1.1.1.4 Challenges and Opportunities in Achieving Sustainable Development 4\u003c\/p\u003e \u003cp\u003e1.1.2 Importance of Disaster Management 5\u003c\/p\u003e \u003cp\u003e1.1.2.1 Definition and Scope of Disaster Management 5\u003c\/p\u003e \u003cp\u003e1.1.2.2 Phases of Disaster Management 6\u003c\/p\u003e \u003cp\u003e1.1.2.3 Types of Disasters 6\u003c\/p\u003e \u003cp\u003e1.1.2.4 Challenges in Disaster Management 6\u003c\/p\u003e \u003cp\u003e1.1.2.5 Importance of Effective Disaster Management 7\u003c\/p\u003e \u003cp\u003e1.1.2.6 Case Studies of Disaster Management 8\u003c\/p\u003e \u003cp\u003e1.1.3 Intersection of AI, Sustainable Development, and Disaster Management 9\u003c\/p\u003e \u003cp\u003e1.2 Sustainable Development 9\u003c\/p\u003e \u003cp\u003e1.2.1 Definition and Principles 9\u003c\/p\u003e \u003cp\u003e1.2.2 Historical Context and Evolution 9\u003c\/p\u003e \u003cp\u003e1.2.3 Goals and Global Initiatives (SDGs) 10\u003c\/p\u003e \u003cp\u003e1.3 Disaster Management 10\u003c\/p\u003e \u003cp\u003e1.3.1 Definition and Types of Disasters 10\u003c\/p\u003e \u003cp\u003e1.3.2 Phases of Disaster Management 10\u003c\/p\u003e \u003cp\u003e1.3.3 Challenges in Traditional Disaster Management Approaches 11\u003c\/p\u003e \u003cp\u003e1.4 Role of AI in Sustainable Development 12\u003c\/p\u003e \u003cp\u003e1.4.1 AI Technologies and their Applications 12\u003c\/p\u003e \u003cp\u003e1.4.2 Case Studies of AI in Sustainable Development 12\u003c\/p\u003e \u003cp\u003e1.5 Role of AI in Disaster Management 15\u003c\/p\u003e \u003cp\u003e1.5.1 AI Technologies in Disaster Prediction and Early Warning 15\u003c\/p\u003e \u003cp\u003e1.5.2 AI in Disaster Response and Recovery 15\u003c\/p\u003e \u003cp\u003e1.5.3 Case Studies of AI in Disaster Management 16\u003c\/p\u003e \u003cp\u003e1.6 Integration of AI in Sustainable Disaster Management 17\u003c\/p\u003e \u003cp\u003e1.6.1 Benefits of AI Integration 17\u003c\/p\u003e \u003cp\u003e1.6.2 Framework for AI Integration 18\u003c\/p\u003e \u003cp\u003e1.6.2.1 Identifying Key Areas for AI Application 18\u003c\/p\u003e \u003cp\u003e1.6.2.2 Ensuring Data Accessibility and Quality 18\u003c\/p\u003e \u003cp\u003e1.6.2.3 Fostering Collaboration Among Stakeholders 18\u003c\/p\u003e \u003cp\u003e1.6.2.4 Addressing Ethical Considerations 19\u003c\/p\u003e \u003cp\u003e1.6.2.5 Ensuring Transparency 19\u003c\/p\u003e \u003cp\u003e1.6.3 Challenges and Ethical Considerations 19\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 21\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Earthquake Risk Assessment Using Artificial Intelligence – A Review on Traditional Methods and Artificial Intelligence– Based Methods 25\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJeba Wincy Deborah. W., Karishma. R., D. Pamela, Joses Jenish Smart, Shajin Prince and Bini. D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction to Earthquake Risk Assessment 26\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Seismic Hazards 27\u003c\/p\u003e \u003cp\u003eData Source of Earthquake Risk Assessment 27\u003c\/p\u003e \u003cp\u003eScenario of Earthquake Incidents of the World 28\u003c\/p\u003e \u003cp\u003eScenario of Earthquake Incidents of India 29\u003c\/p\u003e \u003cp\u003eBrief Overview of Earthquake Incidents in India 29\u003c\/p\u003e \u003cp\u003eTraditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis 33\u003c\/p\u003e \u003cp\u003eSeismic Hazard Mapping 34\u003c\/p\u003e \u003cp\u003eGround Motion Prediction 35\u003c\/p\u003e \u003cp\u003eFault Rupture Hazard Analysis 36\u003c\/p\u003e \u003cp\u003eSite-Specific Studies 37\u003c\/p\u003e \u003cp\u003eBuilding Vulnerability Assessment 37\u003c\/p\u003e \u003cp\u003eOrganizations for Earthquake Risk Assessment and Predictions 42\u003c\/p\u003e \u003cp\u003eEarthquake Risk Assessment Using Artificial Intelligence 43\u003c\/p\u003e \u003cp\u003ePrediction of Earthquake Using AI 44\u003c\/p\u003e \u003cp\u003eAlgorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms 45\u003c\/p\u003e \u003cp\u003eMachine Learning Algorithms 45\u003c\/p\u003e \u003cp\u003eMethods for Earthquake Risk Assessment and Prediction Using AI 46\u003c\/p\u003e \u003cp\u003ePattern Recognition in Seismic Data 46\u003c\/p\u003e \u003cp\u003eAnomaly Detection 47\u003c\/p\u003e \u003cp\u003eEarthquake Forecasting Model 47\u003c\/p\u003e \u003cp\u003eData Fusion and Integration 48\u003c\/p\u003e \u003cp\u003eDamage and Impact Assessment 49\u003c\/p\u003e \u003cp\u003eReal-Time Monitoring 50\u003c\/p\u003e \u003cp\u003eEarly Warning Systems 51\u003c\/p\u003e \u003cp\u003eRisk Mitigation 52\u003c\/p\u003e \u003cp\u003eResilience Planning 52\u003c\/p\u003e \u003cp\u003ePredictive Modeling for Earthquake Forecasting Using AI 54\u003c\/p\u003e \u003cp\u003eIntegration of AI Techniques in Seismic Hazard Analysis 55\u003c\/p\u003e \u003cp\u003eConstruction Practices and Urban Planning for Earthquake Assessment Using AI 56\u003c\/p\u003e \u003cp\u003eFuture Scope of Earthquake Risk Assessment and Prediction Using AI 57\u003c\/p\u003e \u003cp\u003eConclusion 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 AI Applications in Earthquake Resistance Using Change in Structural Design 61\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eE. Nirmala, M. Suresh and Sankar Muthu Paramasivam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 62\u003c\/p\u003e \u003cp\u003e3.2 Review of Literature 63\u003c\/p\u003e \u003cp\u003e3.3 Proposed Techniques 64\u003c\/p\u003e \u003cp\u003e3.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes 64\u003c\/p\u003e \u003cp\u003e3.3.2 Earthquake Prediction Using ANN 67\u003c\/p\u003e \u003cp\u003e3.3.3 AI–Neural Network–Based Earthquake Prediction 67\u003c\/p\u003e \u003cp\u003e3.3.4 AI-Based Dynamic Interpretation Network (DIN)– Multilayer Propagation Algorithm for Earthquake Prediction 68\u003c\/p\u003e \u003cp\u003e3.4 AI- and ML-Based Techniques 70\u003c\/p\u003e \u003cp\u003e3.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms 70\u003c\/p\u003e \u003cp\u003e3.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example 71\u003c\/p\u003e \u003cp\u003e3.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake 73\u003c\/p\u003e \u003cp\u003e3.4.4 Seismic Advancement of Prevailing Masonry Structures 73\u003c\/p\u003e \u003cp\u003e3.5 Conclusion and Future Work 74\u003c\/p\u003e \u003cp\u003eBibliography 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models 79\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajasekaran Thangaraj, Pandiyan P., Palanichamy Naveen, Balasubramaniam Vadivel, P. Prakash and S. Manoj Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 80\u003c\/p\u003e \u003cp\u003e4.2 Related Works 82\u003c\/p\u003e \u003cp\u003e4.3 Dataset Description 83\u003c\/p\u003e \u003cp\u003e4.3.1 Dataset Collection 83\u003c\/p\u003e \u003cp\u003e4.3.2 Dataset Preprocessing 83\u003c\/p\u003e \u003cp\u003e4.4 Methodology 84\u003c\/p\u003e \u003cp\u003e4.4.1 Yolo 84\u003c\/p\u003e \u003cp\u003e4.4.2 YOLOv 3 84\u003c\/p\u003e \u003cp\u003e4.4.3 Tiny-YOLOv 4 85\u003c\/p\u003e \u003cp\u003e4.4.4 YOLOv 5 87\u003c\/p\u003e \u003cp\u003e4.5 Model Evaluation Indicators 88\u003c\/p\u003e \u003cp\u003e4.6 Experimental Results 89\u003c\/p\u003e \u003cp\u003e4.7 Discussion 93\u003c\/p\u003e \u003cp\u003e4.8 Conclusion 94\u003c\/p\u003e \u003cp\u003eReferences 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGeetha S. K., Kiruthika J. K., Sathya S., Srisathya K. B., Rajasekaran Thangaraj and R. Devi Priya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003c\/p\u003e \u003cp\u003e5.2 Importance of Intelligent Transportation Systems in Cyclone Resilience 101\u003c\/p\u003e \u003cp\u003e5.3 Early Warning Systems 103\u003c\/p\u003e \u003cp\u003e5.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management 106\u003c\/p\u003e \u003cp\u003e5.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas 108\u003c\/p\u003e \u003cp\u003e5.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control 111\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 117\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 AI-Enhanced Risk Assessment and Mitigation for Mass Movements 121\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Anusha, V. Sathish Kumar, U. Johnson Alengaram, S. Nagamani and N. Srimathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 122\u003c\/p\u003e \u003cp\u003e6.2 Understanding Mass Movements 123\u003c\/p\u003e \u003cp\u003e6.3 Traditional Risk Assessment and Mitigation Methods 124\u003c\/p\u003e \u003cp\u003e6.4 The Role of AI in Risk Assessment 125\u003c\/p\u003e \u003cp\u003e6.5 AI-Enhanced Mitigation Strategies 127\u003c\/p\u003e \u003cp\u003e6.6 Challenges and Ethical Considerations 129\u003c\/p\u003e \u003cp\u003e6.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management 130\u003c\/p\u003e \u003cp\u003e6.8 Case Studies in AI-Enhanced Mass Movement Management 132\u003c\/p\u003e \u003cp\u003e6.9 Conclusions 134\u003c\/p\u003e \u003cp\u003eReferences 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Distributed AI Systems for Disaster Response and Recovery 137\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRavikumar S., Eugene Berna I., Vijay K., J. Jeyalakshmi and Eashaan Manohar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 138\u003c\/p\u003e \u003cp\u003e7.2 Technology Applied in Critical Cases 141\u003c\/p\u003e \u003cp\u003e7.2.1 Disaster Management Architecture 143\u003c\/p\u003e \u003cp\u003e7.2.2 Proposed Framework 144\u003c\/p\u003e \u003cp\u003e7.2.3 Disaster Management Ontology 145\u003c\/p\u003e \u003cp\u003e7.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology 145\u003c\/p\u003e \u003cp\u003e7.4 ml and Deep Learning Methods: An Overview 146\u003c\/p\u003e \u003cp\u003e7.4.1 Convolutional Neural Network 147\u003c\/p\u003e \u003cp\u003e7.4.2 Lstm 148\u003c\/p\u003e \u003cp\u003e7.4.3 Support Vector Machine 148\u003c\/p\u003e \u003cp\u003e7.4.4 ML\/DL Methods for Disaster and Hazard Prediction 148\u003c\/p\u003e \u003cp\u003e7.4.5 ML\/DL Methods for Risk and Vulnerability Assessment 149\u003c\/p\u003e \u003cp\u003e7.4.6 ML\/DL Methods for Disaster Detection 150\u003c\/p\u003e \u003cp\u003e7.4.7 ML\/DL Methods for Disaster Monitoring 150\u003c\/p\u003e \u003cp\u003e7.4.8 ML\/DL Methods for Damage Assessment 150\u003c\/p\u003e \u003cp\u003e7.5 Phases of Disaster Management 151\u003c\/p\u003e \u003cp\u003e7.5.1 Prediction 151\u003c\/p\u003e \u003cp\u003e7.5.2 Detection 152\u003c\/p\u003e \u003cp\u003e7.5.3 Response 152\u003c\/p\u003e \u003cp\u003e7.5.4 Recovery 152\u003c\/p\u003e \u003cp\u003e7.5.5 Before Disaster 152\u003c\/p\u003e \u003cp\u003e7.5.5.1 Risk Assessment 152\u003c\/p\u003e \u003cp\u003e7.5.5.2 Mitigation 153\u003c\/p\u003e \u003cp\u003e7.5.5.3 Prevention 153\u003c\/p\u003e \u003cp\u003e7.5.5.4 Prediction 153\u003c\/p\u003e \u003cp\u003e7.5.5.5 Detection 153\u003c\/p\u003e \u003cp\u003e7.5.6 During Disaster 153\u003c\/p\u003e \u003cp\u003e7.5.6.1 Preparation 154\u003c\/p\u003e \u003cp\u003e7.5.6.2 Management 154\u003c\/p\u003e \u003cp\u003e7.5.6.3 Response 154\u003c\/p\u003e \u003cp\u003e7.5.7 After Disaster 154\u003c\/p\u003e \u003cp\u003e7.5.7.1 Recovery 154\u003c\/p\u003e \u003cp\u003e7.5.7.2 Monitoring 154\u003c\/p\u003e \u003cp\u003e7.5.7.3 Lessons Learned 155\u003c\/p\u003e \u003cp\u003e7.6 Disaster Management and Disaster Resilience 155\u003c\/p\u003e \u003cp\u003e7.7 Applications of AI for Disaster Management 156\u003c\/p\u003e \u003cp\u003e7.8 AI Applications in Disaster Mitigation 156\u003c\/p\u003e \u003cp\u003e7.9 Conclusion 157\u003c\/p\u003e \u003cp\u003eReferences 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Intelligent Reasoning and Decision‐Making in Disaster Scenarios 163\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSreenivasa Chakravarthi Sangapu, Sreenija Reddy D., Likitha D. and Sountharrajan S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 164\u003c\/p\u003e \u003cp\u003e8.2 Types of Natural Disasters 165\u003c\/p\u003e \u003cp\u003e8.3 Impact of Natural Disasters 167\u003c\/p\u003e \u003cp\u003e8.4 Decision-Making in a Disaster Scenario 170\u003c\/p\u003e \u003cp\u003e8.4.1 Disaster Prediction 171\u003c\/p\u003e \u003cp\u003e8.4.2 Decision-Making in Analyzing the Impact of Disaster 171\u003c\/p\u003e \u003cp\u003e8.4.3 Disaster Precautions and Measures 171\u003c\/p\u003e \u003cp\u003e8.4.4 Benefits of Decision-Making in Disaster Scenario 172\u003c\/p\u003e \u003cp\u003e8.4.5 Technology in Decision-Making Process of a Disaster 173\u003c\/p\u003e \u003cp\u003e8.5 AI\/Machine Learning in Decision-Making of Disaster Scenario 174\u003c\/p\u003e \u003cp\u003e8.5.1 AI\/ML in Predisaster Stage 175\u003c\/p\u003e \u003cp\u003e8.5.2 AI\/ML in During Disaster Stage 176\u003c\/p\u003e \u003cp\u003e8.5.3 AI\/ML in Postdisaster Stage 178\u003c\/p\u003e \u003cp\u003e8.6 AI Methods for Disaster Prediction 179\u003c\/p\u003e \u003cp\u003e8.6.1 Cyclone 179\u003c\/p\u003e \u003cp\u003e8.6.2 Drought 180\u003c\/p\u003e \u003cp\u003e8.6.3 Earthquake 184\u003c\/p\u003e \u003cp\u003e8.6.4 Floods 189\u003c\/p\u003e \u003cp\u003e8.6.5 Landslides 192\u003c\/p\u003e \u003cp\u003e8.7 AI Methods to Analyze the Impact of Disasters 195\u003c\/p\u003e \u003cp\u003e8.7.1 Cyclone 196\u003c\/p\u003e \u003cp\u003e8.7.2 Drought 198\u003c\/p\u003e \u003cp\u003e8.7.3 Earthquake 201\u003c\/p\u003e \u003cp\u003e8.7.4 Floods 204\u003c\/p\u003e \u003cp\u003e8.7.5 Landslide 205\u003c\/p\u003e \u003cp\u003e8.8 AI\/ML Methods in Providing Precautionary Measures 210\u003c\/p\u003e \u003cp\u003e8.9 Intelligent Reasoning 214\u003c\/p\u003e \u003cp\u003e8.10 Conclusion 219\u003c\/p\u003e \u003cp\u003eReferences 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 AI Applications in Real-Time Intelligent Automation 229\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Maragatharajan, L. Sathishkumar, G. Vishnuvarthanan and Jun li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 230\u003c\/p\u003e \u003cp\u003e9.2 Related Works 233\u003c\/p\u003e \u003cp\u003e9.3 Proposed Methods 235\u003c\/p\u003e \u003cp\u003e9.3.1 Use of Drones in Disaster Management 236\u003c\/p\u003e \u003cp\u003e9.3.1.1 Understanding Drone Technology 238\u003c\/p\u003e \u003cp\u003e9.3.1.2 Components and Functionality 238\u003c\/p\u003e \u003cp\u003e9.3.1.3 Types and Classifications 239\u003c\/p\u003e \u003cp\u003e9.3.1.4 Applications 239\u003c\/p\u003e \u003cp\u003e9.3.1.5 Challenges and Future Trends 239\u003c\/p\u003e \u003cp\u003e9.3.1.6 Drone Applications in Earthquake Disaster Response 240\u003c\/p\u003e \u003cp\u003e9.3.1.7 Rapid Damage Assessment 240\u003c\/p\u003e \u003cp\u003e9.3.1.8 Search and Rescue Operations 240\u003c\/p\u003e \u003cp\u003e9.3.1.9 Communication and Coordination 240\u003c\/p\u003e \u003cp\u003e9.3.1.10 Environmental Monitoring and Mapping 241\u003c\/p\u003e \u003cp\u003e9.3.2 Flood Disaster Management Using the Flood Detection Secure System 241\u003c\/p\u003e \u003cp\u003e9.3.2.1 Terminologies in FDSS 243\u003c\/p\u003e \u003cp\u003e9.3.2.2 The Process of FDSS 244\u003c\/p\u003e \u003cp\u003e9.3.3 Flood Management Using AI and IoT 246\u003c\/p\u003e \u003cp\u003e9.3.3.1 Architecture 247\u003c\/p\u003e \u003cp\u003e9.4 Conclusion and Future Perspectives 248\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Knowledge Management and Processing in Disaster Management 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Jayaraghavi, L. S. Jayashree, Palanichamy Naveen and M. Saravanan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 252\u003c\/p\u003e \u003cp\u003e10.1.1 Importance of Knowledge Management 252\u003c\/p\u003e \u003cp\u003e10.1.2 Role of AI 253\u003c\/p\u003e \u003cp\u003e10.2 Knowledge Management in Disaster Management 255\u003c\/p\u003e \u003cp\u003e10.2.1 Data Collection 255\u003c\/p\u003e \u003cp\u003e10.2.2 Information Processing 257\u003c\/p\u003e \u003cp\u003e10.2.3 Knowledge Dissemination 259\u003c\/p\u003e \u003cp\u003e10.2.4 Decision Support Systems 262\u003c\/p\u003e \u003cp\u003e10.3 Integration of AI in Disaster Management 265\u003c\/p\u003e \u003cp\u003e10.3.1 Machine Learning Applications 265\u003c\/p\u003e \u003cp\u003e10.3.2 Natural Language Processing 265\u003c\/p\u003e \u003cp\u003e10.3.3 Predictive Analytics 271\u003c\/p\u003e \u003cp\u003e10.4 Challenges and Ethical Considerations 275\u003c\/p\u003e \u003cp\u003e10.4.1 Data Privacy 275\u003c\/p\u003e \u003cp\u003e10.4.2 Bias and Reliability 278\u003c\/p\u003e \u003cp\u003e10.4.3 Resource Allocation 280\u003c\/p\u003e \u003cp\u003e10.5 Future Prospects and Innovations 284\u003c\/p\u003e \u003cp\u003e10.5.1 Technological Advances 284\u003c\/p\u003e \u003cp\u003e10.5.2 Integration with Existing Systems 288\u003c\/p\u003e \u003cp\u003e10.5.3 Global Collaboration 290\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 294\u003c\/p\u003e \u003cp\u003e10.6.1 Summary of Key Points 294\u003c\/p\u003e \u003cp\u003e10.6.2 Call to Action 296\u003c\/p\u003e \u003cp\u003e10.6.3 Future Vision 298\u003c\/p\u003e \u003cp\u003eReferences 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Perception Technologies for Disaster Situations 301\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGanesh Nataraj, K. Mohanasundaram and S. Ramesh Babu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 302\u003c\/p\u003e \u003cp\u003e11.2 Understanding Disaster Situations 303\u003c\/p\u003e \u003cp\u003e11.3 Role of Perception Technologies 305\u003c\/p\u003e \u003cp\u003e11.4 Categories of Perception Disaster Technologies 306\u003c\/p\u003e \u003cp\u003e11.4.1 Remote Sensing and Imaging Technologies 306\u003c\/p\u003e \u003cp\u003e11.4.2 Computer Vision and Image Analysis 307\u003c\/p\u003e \u003cp\u003e11.4.3 Internet of Things (IoT) Sensors 307\u003c\/p\u003e \u003cp\u003e11.4.4 Data Fusion and Integration 308\u003c\/p\u003e \u003cp\u003e11.4.5 Human-Computer Interaction and Decision Support Systems 309\u003c\/p\u003e \u003cp\u003e11.4.6 Ethical and Privacy Considerations 310\u003c\/p\u003e \u003cp\u003e11.4.7 Future Directions and Challenges 311\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 311\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Integration of AI and Software Engineering for Disaster Management: A Multimodal Disaster Identification Perspective 315\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMithrashree V., Sowmya V., Premjith B. and Jyothish Lal G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 316\u003c\/p\u003e \u003cp\u003e12.2 Related Works 318\u003c\/p\u003e \u003cp\u003e12.3 Methodology 320\u003c\/p\u003e \u003cp\u003e12.4 Experiments and Result Discussion 323\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 328\u003c\/p\u003e \u003cp\u003eBibliography 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 An Intelligent AI-Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIndra Priyadharshini S., Thankaraja Raja Sree and Kanmani S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 334\u003c\/p\u003e \u003cp\u003e13.2 Evolution of Autonomous Vehicles 335\u003c\/p\u003e \u003cp\u003e13.3 Role of AI in Autonomous Systems 336\u003c\/p\u003e \u003cp\u003e13.3.1 Architecture Diagram 337\u003c\/p\u003e \u003cp\u003e13.3.2 AI Algorithms for Fault Prediction and Recognition 340\u003c\/p\u003e \u003cp\u003e13.3.2.1 Isolation Forest Algorithm 341\u003c\/p\u003e \u003cp\u003e13.4 Challenges of Artificial Intelligence in Autonomous Systems 344\u003c\/p\u003e \u003cp\u003e13.5 Blockchain Security Measures for Autonomous Vehicles 346\u003c\/p\u003e \u003cp\u003e13.5.1 Secure Autonomous Vehicle Network Using Blockchain 348\u003c\/p\u003e \u003cp\u003e13.6 List of Software\/Tools, Design Techniques and Programming Languages for Autonomus Systems 349\u003c\/p\u003e \u003cp\u003e13.6.1 Case Studies and Practical Implementations in an Autonomous System 351\u003c\/p\u003e \u003cp\u003e13.6.2 Key Findings and Contributions 353\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 353\u003c\/p\u003e \u003cp\u003eReferences 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Industrial Experiences in Crop Cultivation Using AI for Disaster Management 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSagar Rohi, Ishaan Shrikant Kulkarni, Gagan Deep and Geetanjali Rathee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 358\u003c\/p\u003e \u003cp\u003e14.1.1 AI in Agriculture 358\u003c\/p\u003e \u003cp\u003e14.1.2 Contribution 359\u003c\/p\u003e \u003cp\u003e14.2 Related Work 360\u003c\/p\u003e \u003cp\u003e14.3 Proposed Framework 362\u003c\/p\u003e \u003cp\u003e14.3.1 Construction of Knowledge Graph 363\u003c\/p\u003e \u003cp\u003e14.4 Performance Analysis 364\u003c\/p\u003e \u003cp\u003e14.4.1 Crop Query Dataset 364\u003c\/p\u003e \u003cp\u003e14.4.2 Results Discussion 364\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 366\u003c\/p\u003e \u003cp\u003eReferences 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Comprehensive Review on Robotics in Disaster Response and Recovery 369\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Sarathkumar Sebastin, Sivaraman and V. K. Kuberaganapathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 370\u003c\/p\u003e \u003cp\u003e15.1.1 Role of Robotics in Disaster Response 370\u003c\/p\u003e \u003cp\u003e15.1.2 Role of Robotics in Disaster Recovery 371\u003c\/p\u003e \u003cp\u003e15.1.3 The Key Objectives of Reviewing Robotics in Disaster Response and Recovery 372\u003c\/p\u003e \u003cp\u003e15.2 Disaster Response Robotics 372\u003c\/p\u003e \u003cp\u003e15.2.1 Overview of Different Types of Disasters (Natural and Man-Made) 372\u003c\/p\u003e \u003cp\u003e15.2.2 Robotics Technologies Used in Disaster Response 374\u003c\/p\u003e \u003cp\u003e15.3 Robotics in Disaster Recovery 376\u003c\/p\u003e \u003cp\u003e15.3.1 The Transition from the Response to the Recovery Phase in Disaster Management 376\u003c\/p\u003e \u003cp\u003e15.3.2 The Role of Robotics in Postdisaster Recovery 377\u003c\/p\u003e \u003cp\u003e15.3.3 Infrastructure Inspection and Assessment Using Drones and Ground Robots 379\u003c\/p\u003e \u003cp\u003e15.3.4 Debris Clearance and Demolition with Robotic Assistance 380\u003c\/p\u003e \u003cp\u003e15.3.5 Rehabilitation and Reconstruction Aided by Robotics in Construction 382\u003c\/p\u003e \u003cp\u003e15.3.6 Psychological Support Through Robotic Companionship and Therapy 383\u003c\/p\u003e \u003cp\u003e15.3.7 Review of Case Studies or Research Papers Demonstrating the Application and Impact of Robotics in Disaster Recovery Efforts 385\u003c\/p\u003e \u003cp\u003e15.4 Future Directions 387\u003c\/p\u003e \u003cp\u003e15.4.1 Exploration of Emerging Trends and Future Directions in Disaster Robotics Research 387\u003c\/p\u003e \u003cp\u003e15.4.2 Recommendations for Future Research and Development Efforts to Maximize the Potential of Robotics in Disaster Response and Recovery 389\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 390\u003c\/p\u003e \u003cp\u003eReferences 391\u003c\/p\u003e \u003cp\u003eIndex 393\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Business \u0026amp; management [\u003ca title=\"See our other books on Business \u0026amp; management\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Business%20\u0026amp;%20management%20%5BKJ%5D%22\"\u003eKJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433243078936,"sku":"9781394271573","price":166.98,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394271573.jpg?v=1784852904","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/integrating-ai-for-sustainable-disaster-management-building-resilience-and-preventing-catastrophes-hardback-9781394271573","provider":"Freshly Printed Books","version":"1.0","type":"link"}