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Submit a ProposalDisaster Prediction Using Artificial Intelligence
One Line Description
At the intersection of artificial intelligence and disaster management, this essential guide delivers cutting-edge techniques and real-world case studies to help harness AI for faster, life-saving predictions, real-time monitoring, and optimized emergency response.
Description
The frequency and severity of natural hazards are continuously growing, as a result of climate change, population growth, urbanization, and environmental degradation. Effective disaster risk management is a critical component for achieving sustainable development. Artificial intelligence and machine learning have the potential to greatly improve disaster management by utilizing predictive analytics, enabling real-time monitoring, and facilitating effective resource distribution. These technologies assist in anticipating disasters, delivering timely alerts, and streamlining response initiatives. AI can predict, prepare and respond to crises with speed and accuracy. This book provides comprehensive of the emerging techniques in this vital industry. It discusses different aspects like hazard mapping, real-time event detection and monitoring, and the prediction of extreme events using AI and machine learning. Real-world case studies demonstrate the role of AI in disaster management for managing resources, coordinating emergency services, and logistics optimization. This volume is an essential guide for professionals, researchers, and students, providing a thorough understanding of how artificial intelligence is revolutionizing disaster management, from preparedness and prediction to reaction and recovery.
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The frequency and severity of natural hazards are continuously growing, as a result of climate change, population growth, urbanization, and environmental degradation. Effective disaster risk management is a critical component for achieving sustainable development. Artificial intelligence and machine learning have the potential to greatly improve disaster management by utilizing predictive analytics, enabling real-time monitoring, and facilitating effective resource distribution. These technologies assist in anticipating disasters, delivering timely alerts, and streamlining response initiatives. AI can predict, prepare and respond to crises with speed and accuracy. This book provides comprehensive of the emerging techniques in this vital industry. It discusses different aspects like hazard mapping, real-time event detection and monitoring, and the prediction of extreme events using AI and machine learning. Real-world case studies demonstrate the role of AI in disaster management for managing resources, coordinating emergency services, and logistics optimization. This volume is an essential guide for professionals, researchers, and students, providing a thorough understanding of how artificial intelligence is revolutionizing disaster management, from preparedness and prediction to reaction and recovery.
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Author / Editor Details
Disha Thakur, PhD is an Assistant Professor of Civil Engineering at the University Institute of Technology. She has published 16 research papers and book chapters in reputed international journals and conferences. Her research interests include geotechnical engineering and solid waste management.
Disha Thakur, PhD is an Assistant Professor of Civil Engineering at the University Institute of Technology. She has published 16 research papers and book chapters in reputed international journals and conferences. Her research interests include geotechnical engineering and solid waste management.
Chander Prakash, PhD is an Associate Professor in the Civil Engineering Department at the National Institute of Technology, Hamirpur, India. He has published more than 25 research papers in reputed national and international journals and conferences. His research interests include the application of remotely sensed satellite imagery and GIS in natural resource management, hazard analysis, and planning and management of development projects.
Sanjay Sharma, PhD is an Assistant Professor in the University Institute of Technology at Himachal Pradesh University. He has published 24 research papers in reputed national and international journals and 18 research papers in international conferences. His area of research includes power systems, renewable energy, network planning, microgrid, optimization, GIS, and machine learning.
Himanshu Sharma, PhD is an Associate Professor at the G.H. Raisoni College of Engineering and Management, Nagpur. He has published more than ten research papers in refereed journals and conferences. His area of expertise includes power electronics, machine learning, and optimization techniques.
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Table of Contents
Preface
1. Introduction to AI: Applications in Disaster Prediction
Shivi Sharma and D. D. Sharma
1.1 Introduction
1.1.1 Artificial Intelligence (AI)
1.1.2 Historical Evolution of AI
1.1.3 Importance of AI in Contemporary Society
1.1.4 Defining Intelligence in Machines
1.1.5 AI and Human Cognition
1.1.6 Disaster Management’s Significance
1.2 Intelligent System Components
1.2.1 Machine Learning (ML)
1.2.2 Deep Learning
1.2.3 Natural Language Processing
1.2.4 Fuzzy Logic
1.2.5 Expert Systems
1.2.6 Computer Vision
1.2.7 Robotics and Autonomous Systems
1.3 Emerging Trends in Artificial Intelligence
1.3.1 Generative AI
1.3.2 Integration of Intelligent Systems with the Internet of Things (IoT)
1.3.3 Edge AI
1.3.4 Explainable AI (XAI)
1.3.5 Ethical and Responsible AI
1.3.6 AI in Critical Sectors
1.3.7 Quantum AI
1.4 AI Algorithms and Their Applications
1.4.1 Decision Trees
1.4.2 Random Forest
1.4.3 Support Vector Machines (SVMs)
1.4.4 k-Nearest Neighbors (k-NN)
1.4.5 Neural Networks
1.4.6 Ensemble Methods
1.4.7 Genetic Algorithms (GAs)
1.4.8 Swarm Intelligence
1.4.9 Bayesian Networks
1.4.10 Reinforcement Learning (RL)
1.5 Advantages of Intelligent Systems
1.5.1 Automation and Efficiency
1.5.2 Accuracy and Predictive Power
1.5.3 Scalability and Big Data Integration
1.5.4 Real-Time Decision-Making
1.5.5 Cost Reduction
1.5.6 Continuous Learning and Adaptability
1.5.7 Enhanced Human Decision-Making
1.5.8 Accessibility and Inclusivity
1.5.9 Innovation and New Applications
1.6 Disadvantages and Challenges of Intelligent Systems
1.6.1 Data Dependency and Quality Issues
1.6.2 Lack of Transparency and Explainability
1.6.3 Intensive Computing and Energy Requirements
1.6.4 Ethical and Societal Concerns
1.6.5 Security Vulnerabilities
1.6.6 Economic and Workforce Impacts
1.6.7 Dependence on Technology
1.6.8 Difficulties with Law and Regulation
1.6.9 Environmental Impact
1.7 Summary and Conclusion
References
2. Machine Learning-Based Wildfire Susceptibility Assessment in Southern California
Divyansh Sharma and Disha Thakur
2.1 Introduction
2.2 Study Area
2.3 Methodology
2.4 Results and Discussion
2.5 Limitations and Future Scope of Work
2.6 Conclusion
References
3. Enhancing Disaster Resilience in the Himalayan Region
through AI and Emerging Technologies
Neelam Sidhu, Chandni Thakur, Sahil Sharma, Kanwarpreet Singh and Abhishek Sharma
3.1 Introduction
3.2 Methodology
3.2.1 Research Design and Scope
3.2.2 Inclusion and Exclusion Criteria
3.2.3 Analytical Framework and Thematic Strategy
3.3 Thematic Synthesis of GeoAI Application in Disaster Resilience
3.3.1 Landslides—GeoAI Approaches
3.3.2 Floods and GLOFs—Hydrodynamic, Remote Sensing, and ML Integration
3.3.3 Wildfires—Climate, Land Use, and ML Synergies
3.3.4 GeoAI Enhanced Early Warning Systems (EWSs)
3.3.5 Risk Assessment and Infrastructure Evaluation
3.3.6 Mitigation, Preparedness, and Operational Robotics
3.3.7 Disaster Preparedness, Training, and Community Resilience
3.4 Sociocultural and Geopolitical Implications of AI-Based
Early Warning Systems in the Himalayan Region
3.5 Future Directions for Disaster Management in the IHR
3.5.1 Global Cooperation and Regional Cohesion
3.5.2 Public–Private Partnerships (PPPs)
3.5.3 Training and Capacity Building
3.5.4 Policy Alignment: Mapping GeoAI Developments to the Sendai Framework and the SDGs
3.6 Conclusion
References
4. Long-Term Climate–Vegetation Interactions: Assessing the Influence of Rainfall and Temperature on NDVI in Kullu District, Himachal Pradesh, India (1982–2024)
Vanshika Bhardwaj, Har Amrit Singh Sandhu, Shubham Bhadoriya and Prakul Lath
4.1 Introduction
4.2 Study Area
4.3 Data Used
4.4 Methodology
4.5 Results and Discussion
4.6 Conclusion
References
5. AI-Driven Early Warning and Automated Traffic Control for Infrastructure Resilience During Extreme Events
Akshat Sharma, Amardeep Boora and Richa Sharma
5.1 Introduction
5.1.1 Current Practice
5.1.2 Challenges and Gaps in Current Systems
5.1.3 Objectives and Scope
5.2 Literature Review
5.3 System Architecture
5.3.1 Data Ingestion and Preprocessing Module
5.3.2 Transportation-Asset Inventory with Fragility Parameters
5.3.3 Geospatial Risk-Mapping Engine
5.3.4 Alert Engine and Traffic‐Control Interface
5.3.5 Inspector Feedback Loop and Mobile Reporting
5.4 Implementation
5.4.1 GeoJSON Normalization and Feature Extraction
5.4.2 Risk-Score Computation (CNN-LSTM Hybrid Model)
5.4.3 Notification Workflows and Protocols
5.4.4 Logging, Data Storage, and Retraining Schedule
5.5 Mountain-Valley Cloudburst Scenario
5.5.1 Urban Tunnel Earthquake Scenario
5.6 Conclusion
References
6. ANN-Based Prediction of Road Accident Black Spots
Har Amarit Singh Sandhu, Disha Thakur, Sanjay Kumar, Vedant Singh and Abarar Ahamad
6.1 Introduction
6.1.1 Artificial Neural Network
6.1.2 Importance of the Study
6.2 Study Area
6.2.1 Data Used
6.3 Research Methodology
6.3.1 Prioritizing Black Spots Using ASI
6.3.2 Generation of Accident Prediction Model (APM) Using SPSS
6.3.3 Generation of APM Model Using ANN
6.3.4 Site Visits
6.4 Results and Discussions
6.4.1 Generation of APM Using SPSS
6.4.2 Generation of APM Using ANN
6.4.3 Comparison of the Model
6.5 Policy Suggestions and Remedial Measures
6.5.1 Traffic Light Point (Sector 46/47/48/49)
6.5.2 Airport Light Point
6.5.3 Hallo Majra Light Point
6.6 Conclusions
References
7. Data-Driven AI Model for Predicting Infrastructure Damage from Earthquakes
Swati Singh, Anjali Sharma, Sanjay Kumar, Ankush Tanta and Isha Gupta
7.1 Introduction
7.1.1 Types of Natural Disaster
7.1.1.1 Earthquakes
7.1.1.2 Floods
7.1.1.3 Cyclones
7.1.1.4 Wildfires
7.1.2 Types of Earthquakes
7.1.3 Deep Learning Model
7.2 Literature Survey
7.3 Data and Methods
7.3.1 Dataset
7.3.2 Proposed Methodology
7.3.2.1 Pre-Processing
7.3.2.2 Image Classification Using Convolutional Neural Network (CNN)
7.3.3 Contrast between Model A (8-Layer) vs Model B (12-Layer)
7.4 Key Results and Discussion
7.4.1 CNN A Model Performance Parameter Analysis
7.4.2 CNN B Model Performance Parameter Analyses
7.4.3 Comparative Evaluation of CNN A and CNN B Model Performance
7.5 Conclusion
References
8. Hyperspectral Imaging and Deep Learning for Disaster
Monitoring Systems
P. Shanmuga Sundari, J. Karthikeyan, Sunil Kumar Palaniyappan and R. Vishnu Priya
8.1 Introduction
8.2 Literature Review
8.3 Proposed Methodology
8.3.1 Feature Extraction
8.3.2 3D CNN
8.3.3 R-CNN
8.3.4 Fusion Techniques
8.3.5 U-Net Architect
8.4 Experimental Setup
8.4.1 Dataset Description
8.4.2 Learning Parameter
8.4.3 Evaluation Metric
8.5 Conclusion
References
9. Machine Learning-Based Cloudburst Prediction for Climate
Resilience in Himachal Pradesh
Talari Ganesh
9.1 Introduction
9.2 Literature Review
9.2.1 Research Gaps and Purpose of This Study
9.3 Methodology
9.3.1 Data Collection
9.3.2 Data Cleaning
9.3.3 Feature Engineering
9.3.3.1 Selection of Meteorological Parameters
9.3.3.2 Normalization
9.3.3.3 Transformation
9.3.3.4 Machine Learning Modeling
9.4 Results and Discussion
9.4.1 Comparative Analysis of Model Performance
9.4.2 ROC-AUC Curve
9.4.3 Confusion Matrix Analysis
9.4.4 Misclassification and Its Analysis
9.4.5 Precision and Recall Graph
9.4.6 Feature Importance Analysis
9.5 Conclusion
9.5.1 Summary of Key Findings
9.5.2 Limitations of the Study
9.5.3 Future Research
9.5.4 Practical Consequences and Applications
References
10. Role of AI and Blockchain Technology in Disaster Prediction and Its Management
Supriya Krishnan and Aditya Bharti
10.1 Introduction
10.2 AI and Its Role in Forecasting Disaster and Its Management
10.2.1 Case Studies and Examples of Application of AI in Disaster Management
10.2.1.1 Early Warning Systems
10.2.1.2 Damage Assessment After Disasters
10.2.1.3 Emergency Response Coordination
10.2.1.4 Social Media Analysis and Disaster Monitoring
10.2.1.5 Post-Disaster Recovery Planning
10.2.1.6 Wildfire and Forest Fire Management
10.2.1.7 AI Tools and Robots for Search and Rescue Operations
10.2.1.8 Pandemic Response
10.2.2 Challenges of Implementing AI in Disaster Management
10.2.3 Solutions to Address Challenges of Implementing AI in Disaster Management
10.3 Blockchain Technology (BT) and Its Role in Forecasting Disaster and Its Management
10.3.1 Case Studies and Examples of Application of Blockchain Technology in Disaster Management
10.3.2 Challenges of Implementing Blockchain Technology in Disaster Management
10.3.3 Solutions to Address Challenges of Implementing Blockchain Technology in Disaster Management
10.4 Combining AI and Blockchain Technology for Disaster
Management
10.4.1 Disaster Prediction and Early Warning Systems
10.4.2 Resource Allocation
10.4.3 Fundraising and Donation Tracking
10.4.4 Search and Rescue Operations
10.4.5 Decision-Making Support
10.4.6 Post-Disaster Recovery
10.5 Benefits of Synergizing AI and BT for Disaster Management
10.5.1 Enhanced Accuracy of Predictions
10.5.2 Fostered Transparency and Accountability
10.6 Real-World Applications of Synergistic Application of AI
and Blockchain Technology in Disaster Management
10.6.1 Smart Aid Distribution During Disaster Relief
10.6.2 Predicting and Mitigating Wildfires
10.6.3 Post-Disaster Identity Verification
10.6.4 Agricultural Crisis Management
10.6.5 Flood Prediction and Water Resource Management
10.6.6 Processing of Insurance Claims After a Disaster
10.6.7 Disaster Data Sharing Platforms
10.6.8 Smart Forecasting and Emergency Fund Raising
10.6.9 Seismic Prediction Systems
10.6.10 Disease Tracking in Post-Disaster Situations
10.7 Conclusions
References
11. Predictive Digital Twin for Disaster Prevention and Management in Smart City
Sandhya Avasthi and Suman Lata Tripathi
11.1 Introduction
11.1.1 Background
11.1.2 Introduction of Digital Twin
11.1.3 Predictive Digital Twin in Smart City
11.2 Predictive Digital Twin Architecture
11.3 Predictive Digital Twin Applications for Smart City
11.3.1 Infrastructure Monitoring
11.3.2 Urban Planning
11.3.3 Traffic Management
11.3.4 Smart Grid Management
11.3.5 Disaster Management and Response
11.3.6 Environment Management
11.4 Predictive Digital Twin Framework for Smart City
11.5 Integration of Data and Predictive Modeling
11.5.1 Heterogeneous Information Networks (HINs)
11.5.2 Deep Learning Models and Use of AI
11.5.3 Example Scenario: Flood in a Smart City
11.6 Challenges in Predictive Digital Twin
11.6.1 Technical Challenges
11.6.2 Non-Technical Challenges
11.7 Conclusion
References
12. A Comprehensive Review on Statistical and Artificial
Intelligence Techniques for Landslide Analysis
Amol Sharma, Chander Prakash and Siddharth Garia
12.1 Introduction
12.2 Landslide Mechanisms
12.2.1 Landslide Types
12.2.2 Landslides Failure Mechanisms
12.2.3 Landslides Inventory Mapping
12.3 Methodology of the Review
12.3.1 Search Criteria and Data Sources
12.3.2 Landslide Susceptibility Assessment
12.3.3 Landslide Vulnerability Assessment
12.3.4 Landslide Risk Assessment
12.4 Conclusion
References
13. Integrating AI, IoT, and Drones for Smart Natural Disaster Monitoring and Response
Vinay Anand and Himanshu Sharma
13.1 Introduction
13.1.1 Background and Motivation
13.1.2 The Role of Artificial Intelligence in Disaster Management
13.1.2.1 Predictive Modeling and Early Warning
13.1.2.2 Real-Time Data Analytics
13.1.2.3 Autonomous Response and Recovery
13.1.2.4 Integration of IoT, Cloud Computing, and Drones
13.1.2.5 Real-Time Decision Support Systems
13.1.2.6 Challenges and Research Opportunities
13.1.2.7 Chapter Organization
13.2 Methodology and Framework Design
13.2.1 System Architecture
13.2.2 AI Model Design
13.2.3 Data Acquisition and Integration
13.2.4 Decision Support and Automation
13.3 Literature Review
13.4 Results, Discussion, and Comparative Analysis
13.5 Challenges, Limitations, and Future Research Directions
13.6 Conclusion
References
14. Prediction to Resilience: Next-Generation Artificial
Intelligence for Disaster Risk Management
Abhishek Sharma, Kanwarpreet Singh, Ajay Dheekwal and Neelam Sidhu
14.1 Introduction
14.2 Data Foundation for AI in Disaster Prediction and Management
14.3 Generative Models for Disaster Prediction and Management
14.4 Data Foundations for AI-Driven Disaster Prediction and Management
14.5 Generative Models for Enhanced Disaster Prediction and Scenario Simulation
14.6 Advancements in Natural Language Processing and Large
Language Models for Disaster Intelligence
14.7 Autonomous Systems and Robotics Response and Management
14.8 Quantum Computing in Disaster Prediction and Management
14.9 Integrated AI System for Holistic Disaster Prediction and Management
14.10 Societal, Ethical, and Governance Consideration in AI-Driven Disaster Management
14.11 Conclusions
References
Index
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Preface
1. Introduction to AI: Applications in Disaster Prediction
Shivi Sharma and D. D. Sharma
1.1 Introduction
1.1.1 Artificial Intelligence (AI)
1.1.2 Historical Evolution of AI
1.1.3 Importance of AI in Contemporary Society
1.1.4 Defining Intelligence in Machines
1.1.5 AI and Human Cognition
1.1.6 Disaster Management’s Significance
1.2 Intelligent System Components
1.2.1 Machine Learning (ML)
1.2.2 Deep Learning
1.2.3 Natural Language Processing
1.2.4 Fuzzy Logic
1.2.5 Expert Systems
1.2.6 Computer Vision
1.2.7 Robotics and Autonomous Systems
1.3 Emerging Trends in Artificial Intelligence
1.3.1 Generative AI
1.3.2 Integration of Intelligent Systems with the Internet of Things (IoT)
1.3.3 Edge AI
1.3.4 Explainable AI (XAI)
1.3.5 Ethical and Responsible AI
1.3.6 AI in Critical Sectors
1.3.7 Quantum AI
1.4 AI Algorithms and Their Applications
1.4.1 Decision Trees
1.4.2 Random Forest
1.4.3 Support Vector Machines (SVMs)
1.4.4 k-Nearest Neighbors (k-NN)
1.4.5 Neural Networks
1.4.6 Ensemble Methods
1.4.7 Genetic Algorithms (GAs)
1.4.8 Swarm Intelligence
1.4.9 Bayesian Networks
1.4.10 Reinforcement Learning (RL)
1.5 Advantages of Intelligent Systems
1.5.1 Automation and Efficiency
1.5.2 Accuracy and Predictive Power
1.5.3 Scalability and Big Data Integration
1.5.4 Real-Time Decision-Making
1.5.5 Cost Reduction
1.5.6 Continuous Learning and Adaptability
1.5.7 Enhanced Human Decision-Making
1.5.8 Accessibility and Inclusivity
1.5.9 Innovation and New Applications
1.6 Disadvantages and Challenges of Intelligent Systems
1.6.1 Data Dependency and Quality Issues
1.6.2 Lack of Transparency and Explainability
1.6.3 Intensive Computing and Energy Requirements
1.6.4 Ethical and Societal Concerns
1.6.5 Security Vulnerabilities
1.6.6 Economic and Workforce Impacts
1.6.7 Dependence on Technology
1.6.8 Difficulties with Law and Regulation
1.6.9 Environmental Impact
1.7 Summary and Conclusion
References
2. Machine Learning-Based Wildfire Susceptibility Assessment in Southern California
Divyansh Sharma and Disha Thakur
2.1 Introduction
2.2 Study Area
2.3 Methodology
2.4 Results and Discussion
2.5 Limitations and Future Scope of Work
2.6 Conclusion
References
3. Enhancing Disaster Resilience in the Himalayan Region
through AI and Emerging Technologies
Neelam Sidhu, Chandni Thakur, Sahil Sharma, Kanwarpreet Singh and Abhishek Sharma
3.1 Introduction
3.2 Methodology
3.2.1 Research Design and Scope
3.2.2 Inclusion and Exclusion Criteria
3.2.3 Analytical Framework and Thematic Strategy
3.3 Thematic Synthesis of GeoAI Application in Disaster Resilience
3.3.1 Landslides—GeoAI Approaches
3.3.2 Floods and GLOFs—Hydrodynamic, Remote Sensing, and ML Integration
3.3.3 Wildfires—Climate, Land Use, and ML Synergies
3.3.4 GeoAI Enhanced Early Warning Systems (EWSs)
3.3.5 Risk Assessment and Infrastructure Evaluation
3.3.6 Mitigation, Preparedness, and Operational Robotics
3.3.7 Disaster Preparedness, Training, and Community Resilience
3.4 Sociocultural and Geopolitical Implications of AI-Based
Early Warning Systems in the Himalayan Region
3.5 Future Directions for Disaster Management in the IHR
3.5.1 Global Cooperation and Regional Cohesion
3.5.2 Public–Private Partnerships (PPPs)
3.5.3 Training and Capacity Building
3.5.4 Policy Alignment: Mapping GeoAI Developments to the Sendai Framework and the SDGs
3.6 Conclusion
References
4. Long-Term Climate–Vegetation Interactions: Assessing the Influence of Rainfall and Temperature on NDVI in Kullu District, Himachal Pradesh, India (1982–2024)
Vanshika Bhardwaj, Har Amrit Singh Sandhu, Shubham Bhadoriya and Prakul Lath
4.1 Introduction
4.2 Study Area
4.3 Data Used
4.4 Methodology
4.5 Results and Discussion
4.6 Conclusion
References
5. AI-Driven Early Warning and Automated Traffic Control for Infrastructure Resilience During Extreme Events
Akshat Sharma, Amardeep Boora and Richa Sharma
5.1 Introduction
5.1.1 Current Practice
5.1.2 Challenges and Gaps in Current Systems
5.1.3 Objectives and Scope
5.2 Literature Review
5.3 System Architecture
5.3.1 Data Ingestion and Preprocessing Module
5.3.2 Transportation-Asset Inventory with Fragility Parameters
5.3.3 Geospatial Risk-Mapping Engine
5.3.4 Alert Engine and Traffic‐Control Interface
5.3.5 Inspector Feedback Loop and Mobile Reporting
5.4 Implementation
5.4.1 GeoJSON Normalization and Feature Extraction
5.4.2 Risk-Score Computation (CNN-LSTM Hybrid Model)
5.4.3 Notification Workflows and Protocols
5.4.4 Logging, Data Storage, and Retraining Schedule
5.5 Mountain-Valley Cloudburst Scenario
5.5.1 Urban Tunnel Earthquake Scenario
5.6 Conclusion
References
6. ANN-Based Prediction of Road Accident Black Spots
Har Amarit Singh Sandhu, Disha Thakur, Sanjay Kumar, Vedant Singh and Abarar Ahamad
6.1 Introduction
6.1.1 Artificial Neural Network
6.1.2 Importance of the Study
6.2 Study Area
6.2.1 Data Used
6.3 Research Methodology
6.3.1 Prioritizing Black Spots Using ASI
6.3.2 Generation of Accident Prediction Model (APM) Using SPSS
6.3.3 Generation of APM Model Using ANN
6.3.4 Site Visits
6.4 Results and Discussions
6.4.1 Generation of APM Using SPSS
6.4.2 Generation of APM Using ANN
6.4.3 Comparison of the Model
6.5 Policy Suggestions and Remedial Measures
6.5.1 Traffic Light Point (Sector 46/47/48/49)
6.5.2 Airport Light Point
6.5.3 Hallo Majra Light Point
6.6 Conclusions
References
7. Data-Driven AI Model for Predicting Infrastructure Damage from Earthquakes
Swati Singh, Anjali Sharma, Sanjay Kumar, Ankush Tanta and Isha Gupta
7.1 Introduction
7.1.1 Types of Natural Disaster
7.1.1.1 Earthquakes
7.1.1.2 Floods
7.1.1.3 Cyclones
7.1.1.4 Wildfires
7.1.2 Types of Earthquakes
7.1.3 Deep Learning Model
7.2 Literature Survey
7.3 Data and Methods
7.3.1 Dataset
7.3.2 Proposed Methodology
7.3.2.1 Pre-Processing
7.3.2.2 Image Classification Using Convolutional Neural Network (CNN)
7.3.3 Contrast between Model A (8-Layer) vs Model B (12-Layer)
7.4 Key Results and Discussion
7.4.1 CNN A Model Performance Parameter Analysis
7.4.2 CNN B Model Performance Parameter Analyses
7.4.3 Comparative Evaluation of CNN A and CNN B Model Performance
7.5 Conclusion
References
8. Hyperspectral Imaging and Deep Learning for Disaster
Monitoring Systems
P. Shanmuga Sundari, J. Karthikeyan, Sunil Kumar Palaniyappan and R. Vishnu Priya
8.1 Introduction
8.2 Literature Review
8.3 Proposed Methodology
8.3.1 Feature Extraction
8.3.2 3D CNN
8.3.3 R-CNN
8.3.4 Fusion Techniques
8.3.5 U-Net Architect
8.4 Experimental Setup
8.4.1 Dataset Description
8.4.2 Learning Parameter
8.4.3 Evaluation Metric
8.5 Conclusion
References
9. Machine Learning-Based Cloudburst Prediction for Climate
Resilience in Himachal Pradesh
Talari Ganesh
9.1 Introduction
9.2 Literature Review
9.2.1 Research Gaps and Purpose of This Study
9.3 Methodology
9.3.1 Data Collection
9.3.2 Data Cleaning
9.3.3 Feature Engineering
9.3.3.1 Selection of Meteorological Parameters
9.3.3.2 Normalization
9.3.3.3 Transformation
9.3.3.4 Machine Learning Modeling
9.4 Results and Discussion
9.4.1 Comparative Analysis of Model Performance
9.4.2 ROC-AUC Curve
9.4.3 Confusion Matrix Analysis
9.4.4 Misclassification and Its Analysis
9.4.5 Precision and Recall Graph
9.4.6 Feature Importance Analysis
9.5 Conclusion
9.5.1 Summary of Key Findings
9.5.2 Limitations of the Study
9.5.3 Future Research
9.5.4 Practical Consequences and Applications
References
10. Role of AI and Blockchain Technology in Disaster Prediction and Its Management
Supriya Krishnan and Aditya Bharti
10.1 Introduction
10.2 AI and Its Role in Forecasting Disaster and Its Management
10.2.1 Case Studies and Examples of Application of AI in Disaster Management
10.2.1.1 Early Warning Systems
10.2.1.2 Damage Assessment After Disasters
10.2.1.3 Emergency Response Coordination
10.2.1.4 Social Media Analysis and Disaster Monitoring
10.2.1.5 Post-Disaster Recovery Planning
10.2.1.6 Wildfire and Forest Fire Management
10.2.1.7 AI Tools and Robots for Search and Rescue Operations
10.2.1.8 Pandemic Response
10.2.2 Challenges of Implementing AI in Disaster Management
10.2.3 Solutions to Address Challenges of Implementing AI in Disaster Management
10.3 Blockchain Technology (BT) and Its Role in Forecasting Disaster and Its Management
10.3.1 Case Studies and Examples of Application of Blockchain Technology in Disaster Management
10.3.2 Challenges of Implementing Blockchain Technology in Disaster Management
10.3.3 Solutions to Address Challenges of Implementing Blockchain Technology in Disaster Management
10.4 Combining AI and Blockchain Technology for Disaster
Management
10.4.1 Disaster Prediction and Early Warning Systems
10.4.2 Resource Allocation
10.4.3 Fundraising and Donation Tracking
10.4.4 Search and Rescue Operations
10.4.5 Decision-Making Support
10.4.6 Post-Disaster Recovery
10.5 Benefits of Synergizing AI and BT for Disaster Management
10.5.1 Enhanced Accuracy of Predictions
10.5.2 Fostered Transparency and Accountability
10.6 Real-World Applications of Synergistic Application of AI
and Blockchain Technology in Disaster Management
10.6.1 Smart Aid Distribution During Disaster Relief
10.6.2 Predicting and Mitigating Wildfires
10.6.3 Post-Disaster Identity Verification
10.6.4 Agricultural Crisis Management
10.6.5 Flood Prediction and Water Resource Management
10.6.6 Processing of Insurance Claims After a Disaster
10.6.7 Disaster Data Sharing Platforms
10.6.8 Smart Forecasting and Emergency Fund Raising
10.6.9 Seismic Prediction Systems
10.6.10 Disease Tracking in Post-Disaster Situations
10.7 Conclusions
References
11. Predictive Digital Twin for Disaster Prevention and Management in Smart City
Sandhya Avasthi and Suman Lata Tripathi
11.1 Introduction
11.1.1 Background
11.1.2 Introduction of Digital Twin
11.1.3 Predictive Digital Twin in Smart City
11.2 Predictive Digital Twin Architecture
11.3 Predictive Digital Twin Applications for Smart City
11.3.1 Infrastructure Monitoring
11.3.2 Urban Planning
11.3.3 Traffic Management
11.3.4 Smart Grid Management
11.3.5 Disaster Management and Response
11.3.6 Environment Management
11.4 Predictive Digital Twin Framework for Smart City
11.5 Integration of Data and Predictive Modeling
11.5.1 Heterogeneous Information Networks (HINs)
11.5.2 Deep Learning Models and Use of AI
11.5.3 Example Scenario: Flood in a Smart City
11.6 Challenges in Predictive Digital Twin
11.6.1 Technical Challenges
11.6.2 Non-Technical Challenges
11.7 Conclusion
References
12. A Comprehensive Review on Statistical and Artificial
Intelligence Techniques for Landslide Analysis
Amol Sharma, Chander Prakash and Siddharth Garia
12.1 Introduction
12.2 Landslide Mechanisms
12.2.1 Landslide Types
12.2.2 Landslides Failure Mechanisms
12.2.3 Landslides Inventory Mapping
12.3 Methodology of the Review
12.3.1 Search Criteria and Data Sources
12.3.2 Landslide Susceptibility Assessment
12.3.3 Landslide Vulnerability Assessment
12.3.4 Landslide Risk Assessment
12.4 Conclusion
References
13. Integrating AI, IoT, and Drones for Smart Natural Disaster Monitoring and Response
Vinay Anand and Himanshu Sharma
13.1 Introduction
13.1.1 Background and Motivation
13.1.2 The Role of Artificial Intelligence in Disaster Management
13.1.2.1 Predictive Modeling and Early Warning
13.1.2.2 Real-Time Data Analytics
13.1.2.3 Autonomous Response and Recovery
13.1.2.4 Integration of IoT, Cloud Computing, and Drones
13.1.2.5 Real-Time Decision Support Systems
13.1.2.6 Challenges and Research Opportunities
13.1.2.7 Chapter Organization
13.2 Methodology and Framework Design
13.2.1 System Architecture
13.2.2 AI Model Design
13.2.3 Data Acquisition and Integration
13.2.4 Decision Support and Automation
13.3 Literature Review
13.4 Results, Discussion, and Comparative Analysis
13.5 Challenges, Limitations, and Future Research Directions
13.6 Conclusion
References
14. Prediction to Resilience: Next-Generation Artificial
Intelligence for Disaster Risk Management
Abhishek Sharma, Kanwarpreet Singh, Ajay Dheekwal and Neelam Sidhu
14.1 Introduction
14.2 Data Foundation for AI in Disaster Prediction and Management
14.3 Generative Models for Disaster Prediction and Management
14.4 Data Foundations for AI-Driven Disaster Prediction and Management
14.5 Generative Models for Enhanced Disaster Prediction and Scenario Simulation
14.6 Advancements in Natural Language Processing and Large
Language Models for Disaster Intelligence
14.7 Autonomous Systems and Robotics Response and Management
14.8 Quantum Computing in Disaster Prediction and Management
14.9 Integrated AI System for Holistic Disaster Prediction and Management
14.10 Societal, Ethical, and Governance Consideration in AI-Driven Disaster Management
14.11 Conclusions
References
Index
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