Integrating AI for Sustainable Disaster Management : Building Resilience and Preventing Catastrophes.

Format:

Book

Author/Creator:

Naveen, Palanichamy.

Language:

English

Subjects (All):

Disaster relief--Technological innovations.

Disaster relief.

Emergency management--Technological innovations.

Emergency management.

Artificial intelligence.

Physical Description:

1 online resource (418 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2027.

Summary:

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.

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.

Notes:

Electronic book.

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-27160-3

1-394-27158-1

9781394271580

OCLC:

1565285146