Dahiya

Artificial Intelligence in Remote Sensing for Disaster Management

Herausgeber: Singh, Sartajvir; Sharma, Apoorva; Singh, Gurwinder; Dahiya, Neelam

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Wiley
• Seitenzahl: 384
• Erscheinungstermin: 10. Juni 2025
• Englisch
• Abmessung: 229mm x 152mm x 24mm
• Gewicht: 624g
• ISBN-13: 9781394287192
• ISBN-10: 1394287194
• Artikelnr.: 73875622

Autorenporträt

Neelam Dahiya, PhD is an assistant professor in the Department of Computer Applications at Chitkara University, Punjab, India. She has authored over ten articles in international journals and filed more than ten patents with the Indian Patent Office, five of which were granted. She has also reviewed various articles for renowned journals and conferences. Her research interests include remote sensing, digital image processing, deep learning, and hyperspectral imaging. Gurwinder Singh, PhD is an associate professor at the Institute of Computing at Chandigarh University, India. He has internationally published over 35 articles, conference papers, and book chapters, as well as one patent. He also serves as a member of the International Society for Photogrammetry and Remote Sensing and the Indian Society of Remote Sensing. His research interests include remote sensing, digital image processing, agricultural land use classification, machine learning, and deep learning. Sartajvir Singh, PhD is a professor and the Associate Director for the University Institute of Engineering at Chandigarh University, Punjab, India. He has filed over 50 patents with the Indian Patent Office, with over half granted. He has authored over 50 articles in international journals and edited various proceedings for conferences and symposia in addition to serving as an editor for several international journals. His research interests include electronics, remote sensing, and digital image processing. Apoorva Sharma is a digital analyst and assistant professor in the Department of Computer Science and Engineering, Chandigarh University, Punjab, India. She has published three articles in internationally reputed journals and conferences and contributed to innovative wearable and geospatial technologies. Her research interests include remote sensing, digital image processing, agriculture and cryosphere studies, machine learning, and deep learning.

Inhaltsangabe

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