Details

Subject(s)

• Disaster relief—Technological innovations [Browse]
• Emergency management—Technological innovations [Browse]
• Artificial intelligence [Browse]

Summary note

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.

Notes

Electronic book.

Source of description

Description based on publisher supplied metadata and other sources.

Contents

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.

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ISBN

• 1-394-27160-3
• 1-394-27158-1
• 9781394271580

OCLC

• 1565285146
• 1565081441

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