Integrating AI for Sustainable Disaster Management : Building Resilience and Preventing Catastrophes.
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.
Saved in:
Electronic
eBook
| Online Access | Wiley Kauf EBS 2024 If you have troubles accessing this online source please note our information on accessing licensed electronic media. |
|---|---|
| Main Author | |
| Edition | 1st ed. |
| Place, Publisher, Year |
Newark
: John Wiley & Sons, Incorporated
, 2027
|
| Pages | 1 online resource (418 pages) |
| ISBN | 1-394-27160-3 1-394-27158-1 9781394271580 |
| Language | English |
| Additional Information | Electronic book. |
| Additional Information | Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Sustainable Development and Disaster Management -- 1.1 Introduction -- 1.1.1 Overview of Sustainable Development -- 1.1.1.1 Core Concepts of Sustainable Development -- 1.1.1.2 Historical Context of Sustainable Development -- 1.1.1.3 Principles of Sustainable Development -- 1.1.1.4 Challenges and Opportunities in Achieving Sustainable Development -- 1.1.2 Importance of Disaster Management -- 1.1.2.1 Definition and Scope of Disaster Management -- 1.1.2.2 Phases of Disaster Management -- 1.1.2.3 Types of Disasters -- 1.1.2.4 Challenges in Disaster Management -- 1.1.2.5 Importance of Effective Disaster Management -- 1.1.2.6 Case Studies of Disaster Management -- 1.1.3 Intersection of AI, Sustainable Development, and Disaster Management -- 1.2 Sustainable Development -- 1.2.1 Definition and Principles -- 1.2.2 Historical Context and Evolution -- 1.2.3 Goals and Global Initiatives (SDGs) -- 1.3 Disaster Management -- 1.3.1 Definition and Types of Disasters -- 1.3.2 Phases of Disaster Management -- 1.3.3 Challenges in Traditional Disaster Management Approaches -- 1.4 Role of AI in Sustainable Development -- 1.4.1 AI Technologies and their Applications -- 1.4.2 Case Studies of AI in Sustainable Development -- 1.5 Role of AI in Disaster Management -- 1.5.1 AI Technologies in Disaster Prediction and Early Warning -- 1.5.2 AI in Disaster Response and Recovery -- 1.5.3 Case Studies of AI in Disaster Management -- 1.6 Integration of AI in Sustainable Disaster Management -- 1.6.1 Benefits of AI Integration -- 1.6.2 Framework for AI Integration -- 1.6.2.1 Identifying Key Areas for AI Application -- 1.6.2.2 Ensuring Data Accessibility and Quality -- 1.6.2.3 Fostering Collaboration Among Stakeholders -- 1.6.2.4 Addressing Ethical Considerations. 1.6.2.5 Ensuring Transparency -- 1.6.3 Challenges and Ethical Considerations -- 1.7 Conclusion -- References -- Chapter 2 Earthquake Risk Assessment Using Artificial Intelligence - A Review on Traditional Methods and Artificial Intelligence-Based Methods -- Introduction to Earthquake Risk Assessment -- Understanding Seismic Hazards -- Data Source of Earthquake Risk Assessment -- Scenario of Earthquake Incidents of the World -- Scenario of Earthquake Incidents of India -- Brief Overview of Earthquake Incidents in India -- Traditional Methods Used in Earthquake Risk Assessment and Predictions: Historical Data Analysis -- Seismic Hazard Mapping -- Ground Motion Prediction -- Fault Rupture Hazard Analysis -- Site-Specific Studies -- Building Vulnerability Assessment -- Organizations for Earthquake Risk Assessment and Predictions -- Earthquake Risk Assessment Using Artificial Intelligence -- Prediction of Earthquake Using AI -- Algorithms Used for Earthquake Risk Assessment and Predictions: Deep Learning Algorithms -- Machine Learning Algorithms -- Methods for Earthquake Risk Assessment and Prediction Using AI -- Pattern Recognition in Seismic Data -- Anomaly Detection -- Earthquake Forecasting Model -- Data Fusion and Integration -- Damage and Impact Assessment -- Real-Time Monitoring -- Early Warning Systems -- Risk Mitigation -- Resilience Planning -- Predictive Modeling for Earthquake Forecasting Using AI -- Integration of AI Techniques in Seismic Hazard Analysis -- Construction Practices and Urban Planning for Earthquake Assessment Using AI -- Future Scope of Earthquake Risk Assessment and Prediction Using AI -- Conclusion -- References -- Chapter 3 AI Applications in Earthquake Resistance Using Change in Structural Design -- 3.1 Introduction -- 3.2 Review of Literature -- 3.3 Proposed Techniques. 3.3.1 Different Techniques Used in Structural Design to Reduce Risk in Posterior Earthquakes -- 3.3.2 Earthquake Prediction Using ANN -- 3.3.3 AI-Neural Network.Based Earthquake Prediction -- 3.3.4 AI-Based Dynamic Interpretation Network (DIN)-Multilayer Propagation Algorithm for Earthquake Prediction -- 3.4 AI- and ML-Based Techniques -- 3.4.1 Earthquakes of Smaller Size Can Predict Large-Size Earthquakes Using Substance of AI Machine Learning Algorithms -- 3.4.2 AI-Assisted Simulation-Driven Earthquake-Resistant Design Framework: Taking a Strong Back System as an Example -- 3.4.3 Guidelines for Architectural Design Changes to Predict from Earthquake -- 3.4.4 Seismic Advancement of Prevailing Masonry Structures -- 3.5 Conclusion and Future Work -- Bibliography -- Chapter 4 Automatic Detection of Tropical Cyclones from Satellite Images Using YOLO Models -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Dataset Description -- 4.3.1 Dataset Collection -- 4.3.2 Dataset Preprocessing -- 4.4 Methodology -- 4.4.1 YOLO -- 4.4.2 YOLOv3 -- 4.4.3 Tiny-YOLOv4 -- 4.4.4 YOLOv5 -- 4.5 Model Evaluation Indicators -- 4.6 Experimental Results -- 4.7 Discussion -- 4.8 Conclusion -- References -- Chapter 5 Intelligent Transportation Systems in Cyclone-Prone Areas: A Study and Future Perspectives -- 5.1 Introduction -- 5.2 Importance of Intelligent Transportation Systems in Cyclone Resilience -- 5.3 Early Warning Systems -- 5.4 Applications of Unmanned Aerial Vehicles and Robots in Disaster Management -- 5.5 Emerging Technologies and Future Trends in ITSs for Cyclone-Prone Areas -- 5.6 Optimizing Mobility: Advanced Approaches to Traffic Management and Control -- 5.7 Conclusion -- References -- Chapter 6 AI-Enhanced Risk Assessment and Mitigation for Mass Movements -- 6.1 Introduction -- 6.2 Understanding Mass Movements. 6.3 Traditional Risk Assessment and Mitigation Methods -- 6.4 The Role of AI in Risk Assessment -- 6.5 AI-Enhanced Mitigation Strategies -- 6.6 Challenges and Ethical Considerations -- 6.7 Future Trends and Innovations in AI-Enhanced Mass Movement Management -- 6.8 Case Studies in AI-Enhanced Mass Movement Management -- 6.9 Conclusions -- References -- Chapter 7 Distributed AI Systems for Disaster Response and Recovery -- 7.1 Introduction -- 7.2 Technology Applied in Critical Cases -- 7.2.1 Disaster Management Architecture -- 7.2.2 Proposed Framework -- 7.2.3 Disaster Management Ontology -- 7.3 Approach to Disaster Relief That is Enabled by Information and Communication Technology -- 7.4 ML and Deep Learning Methods: An Overview -- 7.4.1 Convolutional Neural Network -- 7.4.2 LSTM -- 7.4.3 Support Vector Machine -- 7.4.4 ML/DL Methods for Disaster and Hazard Prediction -- 7.4.5 ML/DL Methods for Risk and Vulnerability Assessment -- 7.4.6 ML/DL Methods for Disaster Detection -- 7.4.7 ML/DL Methods for Disaster Monitoring -- 7.4.8 ML/DL Methods for Damage Assessment -- 7.5 Phases of Disaster Management -- 7.5.1 Prediction -- 7.5.2 Detection -- 7.5.3 Response -- 7.5.4 Recovery -- 7.5.5 Before Disaster -- 7.5.5.1 Risk Assessment -- 7.5.5.2 Mitigation -- 7.5.5.3 Prevention -- 7.5.5.4 Prediction -- 7.5.5.5 Detection -- 7.5.6 During Disaster -- 7.5.6.1 Preparation -- 7.5.6.2 Management -- 7.5.6.3 Response -- 7.5.7 After Disaster -- 7.5.7.1 Recovery -- 7.5.7.2 Monitoring -- 7.5.7.3 Lessons Learned -- 7.6 Disaster Management and Disaster Resilience -- 7.7 Applications of AI for Disaster Management -- 7.8 AI Applications in Disaster Mitigation -- 7.9 Conclusion -- References -- Chapter 8 Intelligent Reasoning and Decision.Making in Disaster Scenarios -- 8.1 Introduction -- 8.2 Types of Natural Disasters -- 8.3 Impact of Natural Disasters. 8.4 Decision-Making in a Disaster Scenario -- 8.4.1 Disaster Prediction -- 8.4.2 Decision-Making in Analyzing the Impact of Disaster -- 8.4.3 Disaster Precautions and Measures -- 8.4.4 Benefits of Decision-Making in Disaster Scenario -- 8.4.5 Technology in Decision-Making Process of a Disaster -- 8.5 AI/Machine Learning in Decision-Making of Disaster Scenario -- 8.5.1 AI/ML in Predisaster Stage -- 8.5.2 AI/ML in During Disaster Stage -- 8.5.3 AI/ML in Postdisaster Stage -- 8.6 AI Methods for Disaster Prediction -- 8.6.1 Cyclone -- 8.6.2 Drought -- 8.6.3 Earthquake -- 8.6.4 Floods -- 8.6.5 Landslides -- 8.7 AI Methods to Analyze the Impact of Disasters -- 8.7.1 Cyclone -- 8.7.2 Drought -- 8.7.3 Earthquake -- 8.7.4 Floods -- 8.7.5 Landslide -- 8.8 AI/ML Methods in Providing Precautionary Measures -- 8.9 Intelligent Reasoning -- 8.10 Conclusion -- References -- Chapter 9 AI Applications in Real-Time Intelligent Automation -- 9.1 Introduction -- 9.2 Related Works -- 9.3 Proposed Methods -- 9.3.1 Use of Drones in Disaster Management -- 9.3.1.1 Understanding Drone Technology -- 9.3.1.2 Components and Functionality -- 9.3.1.3 Types and Classifications -- 9.3.1.4 Applications -- 9.3.1.5 Challenges and Future Trends -- 9.3.1.6 Drone Applications in Earthquake Disaster Response -- 9.3.1.7 Rapid Damage Assessment -- 9.3.1.8 Search and Rescue Operations -- 9.3.1.9 Communication and Coordination -- 9.3.1.10 Environmental Monitoring and Mapping -- 9.3.2 Flood Disaster Management Using the Flood Detection Secure System -- 9.3.2.1 Terminologies in FDSS -- 9.3.2.2 The Process of FDSS -- 9.3.3 Flood Management Using AI and IoT -- 9.3.3.1 Architecture -- 9.4 Conclusion and Future Perspectives -- References -- Chapter 10 Knowledge Management and Processing in Disaster Management -- 10.1 Introduction -- 10.1.1 Importance of Knowledge Management -- 10.1.2 Role of AI. 10.2 Knowledge Management in Disaster Management. |
Internet
Wiley Kauf EBS 2024If you have troubles accessing this online source please note our information on accessing licensed electronic media.