Invest in Artificial Intelligence in Remote Sensing for Disaster Management to gain invaluable insights into cutting-edge AI technologies and their transformative role in effectively monitoring and managing natural disasters. Artificial Intelligence in Remote Sensing for Disaster Management examines the involvement of advanced tools and technologies such as Artificial Intelligence in disaster management with remote sensing. Remote sensing offers cost-effective, quick assessments and responses to natural disasters. In the past few years, many advances have been made in the monitoring and mapping of natural disasters with the integration of AI in remote sensing. This volume focuses on AI-driven observations of various natural disasters including landslides, snow avalanches, flash floods, glacial lake outburst floods, and earthquakes. There is currently a need for sustainable development, near real-time monitoring, forecasting, prediction, and management of natural resources, flash floods, sea-ice melt, cyclones, forestry, and climate changes. This book will provide essential guidance regarding AI-driven algorithms specifically developed for disaster management to meet the requirements of emerging applications.

Table of Contents:
Preface xvii 1 Introduction to Natural Hazards, Challenges, and Managing Strategies 1 Puninder Kaur, Taruna Sharma, Jaswinder Singh and Neelam Dahiya 1.1 Introduction 1 1.2 Terminology Used 3 1.2.1 Hazard 3 1.2.2 Mitigation 3 1.2.3 Vulnerability 4 1.2.4 Disaster 4 1.2.5 Risk 4 1.3 Classification of Natural Hazards 5 1.3.1 Biological Natural Hazards 5 1.3.2 Geological Hazards 6 1.3.3 Hydrological Hazards 6 1.3.4 Meteorological Hazards 6 1.4 Challenges and Risks of Natural Hazards 7 1.4.1 Loss of Life 7 1.4.2 Property Damage and Economic Losses 8 1.4.3 Disruption of Critical Infrastructure 8 1.4.4 Health Risks and Disease Outbreaks 8 1.4.5 Environmental Degradation 9 1.4.6 Social and Economic Disparities 9 1.4.7 Psychosocial Impacts 9 1.5 Strategies to Prevent Natural Hazards 10 1.5.1 Planning and Regulation for Reducing Risk on Land 10 1.5.1.1 Zoning Regulations 10 1.5.1.2 Building Codes and Standards 10 1.5.1.3 Setback Requirements 11 1.5.1.4 Erosion Control Measures 11 1.5.1.5 Floodplain Management 11 1.5.2 Environmental Conservation and Restoration 11 1.5.2.1 Protecting Natural Ecosystems 11 1.5.2.2 Restoring Degraded Ecosystems 12 1.5.2.3 Floodplain Management 12 1.5.2.4 Coastal Protection 12 1.5.2.5 Sustainable Land Management 12 1.5.3 Early Warning Systems and Preparedness 13 1.5.3.1 Hazard Monitoring and Forecasting 13 1.5.3.2 Risk Assessment and Planning 13 1.5.4 Education and Awareness 13 1.5.4.1 Understanding Hazards and Risks 13 1.5.4.2 Promoting Risk Reduction Measures 14 1.5.4.3 School Curriculum Integration 14 1.5.5 Climate Change Mitigation 14 1.5.5.1 Reducing Greenhouse Gas Emissions 14 1.5.5.2 Promoting Renewable Energy 15 1.5.5.3 Enhancing Energy Efficiency 15 1.6 Role of Remote Sensing Device to Prevent Natural Disasters 15 1.6.1 Hazard Detection and Monitoring 15 1.6.2 Early Warning Systems 16 1.6.3 Risk Assessment and Vulnerability Mapping 16 1.6.4 Environmental Monitoring 16 1.6.5 Mapping and Damage Assessment 16 1.7 Conclusion 17 Acknowledgments 17 References 17 2 Role of Remote Sensing for Emergency Response and Disaster Rehabilitation 21 Mochamad Irwan Hariyono and Aptu Andy Kurniawan 2.1 Introduction 21 2.2 Method 25 2.3 Disaster Management 25 2.4 Result and Discussion 26 2.4.1 Floods 26 2.4.2 Earthquakes 28 2.4.3 Drought 29 2.4.4 Landslides 29 2.4.5 Land/Forest Fire 30 2.4.6 Volcanic Eruption 31 2.5 Conclusion 32 References 33 3 Fundamentals of Disaster Management Using Remote Sensing 35 Garima and Narayan Vyas 3.1 Introduction 35 3.2 Importance of Remote Sensing in Disaster Management 36 3.2.1 Role in Emergency Response 37 3.2.2 Impact on Disaster Rehabilitation 38 3.2.3 Remote Sensing Taxonomy 39 3.3 Remote Sensing Applications in Emergency Response 40 3.3.1 Damage Assessment 40 3.3.1.1 Techniques and Methods 41 3.3.1.2 Integration with Other Data Sources 42 3.3.1.3 Feature Extraction from Pre- and Post- Disaster Imagery 43 3.4 Acquisition of Disaster Features 45 3.4.1 Acquisition of Tsunami Features with Remote Sensing 45 3.4.2 Acquisition of Earthquake Features with Remote Sensing 48 3.4.3 Acquisition of Wildfire Features with Remote Sensing 50 Conclusion 55 References 55 4 Remote Sensing for Monitoring of Disaster-Prone Region 59 Navdeep Singh Sodhi and Sofia Singla 4.1 Introduction 60 4.2 Related Existing Work 63 4.3 Comparison Table 68 4.4 Graphical Analysis 72 4.5 Conclusion and Future Scope 74 Acknowledgments 74 References 75 5 Artificial Intelligence Tools in Disaster Risk Reduction and Emergency Management 79 Rupinder Singh, Manjinder Singh and Jaswinder Singh 5.1 Introduction 80 5.1.1 Role of AI Tools and Technologies 80 5.1.2 Purpose and Objectives of the Research Paper 82 5.2 AI Tools and Technologies in Disaster Risk Reduction 83 5.3 Ethical and Social Implications of Using AI Tools in Disaster Management 91 5.4 Impact and Effectiveness of AI Tools and Technologies 92 5.5 AI for Dismantling Difficulties in Disaster Management 94 5.6 Future Directions and Recommendations 95 5.7 Conclusion 95 Acknowledgments 96 Funding 96 References 96 6 AI Tools and Technologies in Disaster Risk Reduction and Management 99 Alisha Sinha and Laxmi Kant Sharma 6.1 Introduction 100 6.2 AI Tools in Different Phases of Disaster Management 101 6.2.1 Before Disaster 101 6.2.2 During Disaster 102 6.2.3 After Disaster 102 6.3 Use of Geospatial Technologies and AI in Disaster Management 103 6.4 Future Challenges and Goals with AI 116 6.5 Conclusions 116 Acknowledgment 117 References 117 7 AI-Based Landslide Susceptibility Evaluation 125 Amanpreet Singh and Payal Kaushal 7.1 Introduction 126 7.2 Principle of Support Vector Machines (SVM) 128 7.3 Conclusion 132 Acknowledgments 132 References 133 8 Navigating Risk: A Comprehensive Study of Landslide Susceptibility Mapping and Hazard Assessment 139 Gaurav Kumar Saini and Inderdeep Kaur 8.1 Introduction 140 8.1.1 Challenges in Factor Selection and Weighting 141 8.1.2 Combination of Subjective and Objective Approaches 141 8.2 Factors Responsible for Landslides 141 8.2.1 External 141 8.2.2 Internal 142 8.3 Types of Landslides 143 8.4 Landslide Detection Techniques 144 8.5 Landslide Monitoring Techniques 146 8.6 Use of Machine Learning in Landslide Mapping 147 8.7 Use of Deep Learning in Landslide Mapping 148 8.8 Use of Ensemble Techniques 148 8.9 Limitations of Existing Algorithms 149 8.10 Dataset Used 149 8.11 Model Architecture 153 8.12 Results and Discussion 154 Acknowledgment 157 References 158 9 Application of Geospatial Technology for Disaster Risk Reduction Using Machine Learning Algorithm and OpenStreetMap in Batticaloa District, Eastern Province, Sri Lanka 161 Zahir I.L.M., Suthakaran S., Iyoob A.L., Nuskiya M.H.F. and Fowzul Ameer M.L. 9.1 Introduction 162 9.1.1 Geospatial Technology in DRR 163 9.1.2 MLAs in DRR 164 9.1.3 OSM in DRR 164 9.1.4 Integrated Approach of Geospatial Technology, Machine Learning, and OSM 165 9.2 Significance of the Study 165 9.3 Objectives 167 9.4 Methodology 167 9.4.1 Study Area 167 9.4.2 Data Collection 169 9.4.2.1 MLAs for DRR 169 9.4.2.2 Integration with OSM 171 9.5 Results and Discussion 174 9.6 Conclusion and Recommendations 179 References 180 10 Landslide Displacement Forecasting With AI Models 185 Sangeetha Annam 10.1 Introduction 186 10.1.1 Technology Classifications for Remote Sensing 187 10.1.2 Architecture of Risk Management 189 10.2 Artificial Intelligence-Based Forecasting of Landslide Displacement 191 10.3 Performance Metrics 195 10.4 Limitations in Assessing the AI Models for Landslide Displacement Prediction 196 10.5 Technologies Integrated with AI Models 197 10.6 Conclusion 198 References 199 11 Estimation of Snow Avalanche Hazardous Zones With AI Models 201 Rajinder Kaur, Sartajvir Singh and Ganesh Kumar Sethi 11.1 Introduction 202 11.2 Study Site and Data 203 11.3 Methodology 204 11.4 Results and Discussion 208 11.5 Conclusion 209 References 210 12 Predicting and Understanding the Snow Avalanche Event 213 Nitin Arora and Sakshi 12.1 Introduction 214 12.2 Snow Avalanche 214 12.2.1 Types of Snow Avalanche 216 12.2.1.1 Sluff Avalanche 216 12.2.1.2 Slab Avalanche 216 12.2.2 Basic Reason Behind Snow Avalanche 217 12.2.3 Role of Remote Sensing in Snow Avalanche Prediction 218 12.3 Contributory Factors 219 12.3.1 Terrain 220 12.3.2 Precipitation 220 12.3.2.1 Snow Accumulation 220 12.3.2.2 Formation of Weak Layers 220 12.3.2.3 Load and Stress Increases 220 12.3.2.4 Rain-on-Snow Effect 220 12.3.3 Wind Temperature 221 12.3.4 Snowpack Stratigraphy 221 12.4 Remote Sensing and Avalanche Prediction 221 12.4.1 Basic Principle Behind Radar-Based Remote Sensing 222 12.4.2 Need for Remote Sensing 223 12.5 Methodology 223 12.5 Conclusion and Future Scope 225 References 225 13 A Systematic Review on Challenges and Opportunities in Snow Avalanche Risk Assessment and Analysis 229 Apoorva Sharma, Bhavneet Kaur and Sartajvir Singh 13.1 Introduction 230 13.2 Advanced Tools for Snow Avalanche Monitoring System 233 13.3 Snow Avalanche Risk Assessment and Analysis 234 13.4 Challenges in Snow Avalanche Risk Assessment and Analysis 237 13.5 Opportunities in Snow Avalanche Risk Assessment and Analysis 237 13.6 Summary 239 References 239 14 AI-Based Modeling of GLOF Process and Its Impact 243 Jaswinder Singh, Rajwinder Kaur, Puninder Kaur and Rupinder Singh 14.1 Introduction 244 14.1.1 The Andes 245 14.1.2 High Mountain Asia (HMA) 245 14.1.3 Other Regions 245 14.2 Artificial Intelligence and GLOF 246 14.2.1 Modeling the GLOF Process 246 14.2.2 Impact Assessment 246 14.2.3 Benefits of Using AI 247 14.2.4 AI Techniques for the Prediction of GLOF 247 14.2.4.1 Machine Learning (ML) 248 14.2.4.2 Deep Learning (DL) 248 14.2.4.3 Time Series Analysis 248 14.2.4.4 Integration with Other Techniques 249 14.3 Machine Learning Techniques for GLOF 249 14.3.1 Use of Supervised Learning in GLOF 249 14.3.1.1 Data Preparation 249 14.3.1.2 Feature Engineering 250 14.3.1.3 Model Training 250 14.3.1.4 Prediction 250 14.3.1.5 Benefits of Using Supervised Learning for GLOF Prediction 250 14.3.1.6 Various Supervised Algorithms for the GLOF Process 251 14.3.1.7 Choosing the Right Algorithm 252 14.3.2 Use of Unsupervised Learning in GLOF 253 14.3.2.1 Anomaly Detection 253 14.3.2.2 Feature Discovery 254 14.3.2.3 Data Preprocessing 254 14.3.2.4 Unsupervised Learning Algorithms for GLOF Analysis 255 14.3.2.5 Choosing the Right Algorithm 256 14.3.2.6 Objective 257 14.3.2.7 Data Characteristics 257 14.3.2.8 Benefits of Using Unsupervised Learning for GLOF 257 14.3.2.9 Challenges and Considerations 257 14.4 Deep Learning for GLOF Modeling 258 14.4.1 Convolutional Neural Networks (CNNs) 258 14.4.2 Recurrent Neural Networks (RNNs) 258 14.4.3 Combining Different Deep Learning Techniques 259 14.5 Existing Models for GLOF Modeling: A Comparison 260 14.5.1 Statistical Models 260 14.5.2 Machine Learning Models 261 14.5.3 Deep Learning Models 261 14.5.4 Comparison 262 14.5.5 Choosing the Right Model 262 14.5.6 Additional Considerations 262 14.6 Future Models for GLOF Modeling 263 14.6.1 Integration of Diverse Data Sources 263 14.6.2 Explainable AI (XAI) 263 14.6.3 Advanced Deep Learning Techniques 264 14.6.4 Integration with Physical Modeling 264 14.7 AI Challenges and Limitations 265 14.8 Insights and Findings from AI-Based Modeling of GLOF Processes 265 14.9 Evaluation of Methodology Used for AI-Based Modeling of GLOF Processes 266 14.10 Conclusion 268 References 268 15 A Systematic Review of the GLOF Susceptibility Assessment Techniques 271 Oushnik Banerjee, Anshu Kumari and Apoorva Shamra 15.1 Introduction 272 15.2 Glacial Lakes in the Western Himalayas 273 15.2.1 Gangotri Glacier (Supra Glacial Lake) 274 15.2.2 Samudra Tapu (Pro Glacial Lake) 275 15.2.3 South Lhonak Lake (Unconnected Glacial- Fed Lake) 275 15.2.4 Dal Lake (Non-Glacial-Fed) 275 15.3 Sensitive Glacial Lake in the Western Himalayas 276 15.3.1 Samudra Tapu Glacier 276 15.4 GLOF Susceptibility Mapping Techniques 277 15.4.1 Satellite Imagery Analysis 277 15.4.2 Semi-Automated GLOF Susceptibility Assessment System 278 15.4.3 Glacial Lake Mapping 279 15.5 Stages of Glaciations 279 15.6 Glacier Retreat 281 15.7 Causes of Glacial Lake Change 282 15.8 Depiction and Categorization of Glacial Lakes 282 15.9 Study of Evaluating Parameters 283 15.9.1 Sensitivity Evaluation 283 15.9.2 Calculation of Weights and GLOF Susceptibility Index 283 15.10 Summary 284 Acknowledgment 285 References 285 16 Challenges of GLOF Estimation and Prediction 289 Neelam Dahiya, Sartajvir Singh and Puninder Kaur 16.1 Introduction 290 16.2 Types of GLOF 291 16.2.1 Glacial Lakes 291 16.2.2 Moraine-Dammed Lake 291 16.2.3 Ice-Dammed Lakes 292 16.3 Reasons for GLOF Occurrence 292 16.3.1 Glacial Retreat 292 16.3.2 Geothermal Activity 293 16.3.3 Avalanches 293 16.3.4 Earthquakes and Landslides 294 16.3.5 Human Activities 294 16.3.6 Glacial Moraine Failure 295 16.3.7 Glacier Lake Expansion 295 16.3.8 Glacier Surging and Calving 295 16.4 Challenges Faced for GLOF Estimation 296 16.4.1 Early Detection 296 16.4.2 Infrastructure Damage 297 16.4.3 Loss of Life 297 16.4.4 Economic Impact 298 16.4.5 Environmental Degradation 298 16.4.6 Climate Changes 299 16.5 GLOF Solution 299 16.6 Conclusion 299 References 300 17 Real-Time Earthquake Monitoring with Remote Sensing and AI Technology 303 Koushik Sundar, Narayan Vyas and Neha Bhati 17.1 Introduction 304 17.2 Basics of AI and Remote Sensing 305 17.2.1 AI Applications in Earthquake Monitoring 306 17.2.1.1 Optical Remote Sensing 306 17.2.1.2 Microwave Remote Sensing 307 17.2.2 Satellites and Sensors 308 17.2.3 AI and Remote Sensing for Integration in Monitoring Earthquakes 308 17.2.4 Challenges and Future Directions 310 17.3 Advances in Satellite Remote Sensing Techniques for Improved Earthquake Monitoring 310 17.3.1 Comparative Analysis of Remote Sensing Satellites 310 17.3.2 Comparison of Optical and Microwave Satellite Imagery 311 17.3.3 Case Study on Pre- and Post-images of Earthquake in Doti District of Nepal 313 17.4 How AI Is Currently Being Used in Remote Sensing to Monitor Earthquakes 315 17.4.1 Automated Image Processing 315 17.4.2 Seismic Data Augmentation 316 17.4.3 Risk Assessment and Management 316 17.4.4 Integrated Monitoring Systems 317 17.5 Ongoing and Future Practical AI Applications in Remote Sensing 318 17.5.1 More Sophisticated Prediction Models 318 17.5.2 Real-Time Data Processing 318 17.5.3 Damage and Recovery 319 17.5.4 Public Safety and Community Resilience 319 17.6 Conclusion 320 References 321 18 Enhancing Seismic-Events Identification and Analysis Using Machine Learning Approach 323 Gurwinder Singh, Harun and Tejinder Pal Singh 18.1 Introduction 324 18.2 Methodology 326 18.3 Results and Discussion 329 18.3.1 ml Models 333 18.3.2 ARIMA Models 334 18.3.3 Neural Network Models 335 18.3.4 Spatial Analysis 338 18.4 Limitations 340 18.5 Future Directions 340 18.6 Conclusion and Future Scope 341 References 341 Index 343