{"product_id":"artificial-intelligence-in-remote-sensing-for-disaster-management-hardback-9781394287192","title":"Artificial Intelligence in Remote Sensing for Disaster Management (Hardback) 9781394287192","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence in Remote Sensing for Disaster Management\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eNeelam Dahiya (Edited by), Singh (Author), Gurwinder Singh (Edited by), Sartajvir Singh (Edited by), Apoorva Sharma (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394287192, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 10 June 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e28 x 19 x 2.5 cm, 0.624 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eInvest in \u003ci\u003eArtificial Intelligence in Remote Sensing for Disaster Management\u003c\/i\u003e to gain invaluable insights into cutting-edge AI technologies and their transformative role in effectively monitoring and managing natural disasters.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eArtificial Intelligence in Remote Sensing for Disaster Management\u003c\/i\u003e 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Natural Hazards, Challenges, and Managing Strategies 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePuninder Kaur, Taruna Sharma, Jaswinder Singh and Neelam Dahiya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Terminology Used 3\u003c\/p\u003e \u003cp\u003e1.2.1 Hazard 3\u003c\/p\u003e \u003cp\u003e1.2.2 Mitigation 3\u003c\/p\u003e \u003cp\u003e1.2.3 Vulnerability 4\u003c\/p\u003e \u003cp\u003e1.2.4 Disaster 4\u003c\/p\u003e \u003cp\u003e1.2.5 Risk 4\u003c\/p\u003e \u003cp\u003e1.3 Classification of Natural Hazards 5\u003c\/p\u003e \u003cp\u003e1.3.1 Biological Natural Hazards 5\u003c\/p\u003e \u003cp\u003e1.3.2 Geological Hazards 6\u003c\/p\u003e \u003cp\u003e1.3.3 Hydrological Hazards 6\u003c\/p\u003e \u003cp\u003e1.3.4 Meteorological Hazards 6\u003c\/p\u003e \u003cp\u003e1.4 Challenges and Risks of Natural Hazards 7\u003c\/p\u003e \u003cp\u003e1.4.1 Loss of Life 7\u003c\/p\u003e \u003cp\u003e1.4.2 Property Damage and Economic Losses 8\u003c\/p\u003e \u003cp\u003e1.4.3 Disruption of Critical Infrastructure 8\u003c\/p\u003e \u003cp\u003e1.4.4 Health Risks and Disease Outbreaks 8\u003c\/p\u003e \u003cp\u003e1.4.5 Environmental Degradation 9\u003c\/p\u003e \u003cp\u003e1.4.6 Social and Economic Disparities 9\u003c\/p\u003e \u003cp\u003e1.4.7 Psychosocial Impacts 9\u003c\/p\u003e \u003cp\u003e1.5 Strategies to Prevent Natural Hazards 10\u003c\/p\u003e \u003cp\u003e1.5.1 Planning and Regulation for Reducing Risk on Land 10\u003c\/p\u003e \u003cp\u003e1.5.1.1 Zoning Regulations 10\u003c\/p\u003e \u003cp\u003e1.5.1.2 Building Codes and Standards 10\u003c\/p\u003e \u003cp\u003e1.5.1.3 Setback Requirements 11\u003c\/p\u003e \u003cp\u003e1.5.1.4 Erosion Control Measures 11\u003c\/p\u003e \u003cp\u003e1.5.1.5 Floodplain Management 11\u003c\/p\u003e \u003cp\u003e1.5.2 Environmental Conservation and Restoration 11\u003c\/p\u003e \u003cp\u003e1.5.2.1 Protecting Natural Ecosystems 11\u003c\/p\u003e \u003cp\u003e1.5.2.2 Restoring Degraded Ecosystems 12\u003c\/p\u003e \u003cp\u003e1.5.2.3 Floodplain Management 12\u003c\/p\u003e \u003cp\u003e1.5.2.4 Coastal Protection 12\u003c\/p\u003e \u003cp\u003e1.5.2.5 Sustainable Land Management 12\u003c\/p\u003e \u003cp\u003e1.5.3 Early Warning Systems and Preparedness 13\u003c\/p\u003e \u003cp\u003e1.5.3.1 Hazard Monitoring and Forecasting 13\u003c\/p\u003e \u003cp\u003e1.5.3.2 Risk Assessment and Planning 13\u003c\/p\u003e \u003cp\u003e1.5.4 Education and Awareness 13\u003c\/p\u003e \u003cp\u003e1.5.4.1 Understanding Hazards and Risks 13\u003c\/p\u003e \u003cp\u003e1.5.4.2 Promoting Risk Reduction Measures 14\u003c\/p\u003e \u003cp\u003e1.5.4.3 School Curriculum Integration 14\u003c\/p\u003e \u003cp\u003e1.5.5 Climate Change Mitigation 14\u003c\/p\u003e \u003cp\u003e1.5.5.1 Reducing Greenhouse Gas Emissions 14\u003c\/p\u003e \u003cp\u003e1.5.5.2 Promoting Renewable Energy 15\u003c\/p\u003e \u003cp\u003e1.5.5.3 Enhancing Energy Efficiency 15\u003c\/p\u003e \u003cp\u003e1.6 Role of Remote Sensing Device to Prevent Natural Disasters 15\u003c\/p\u003e \u003cp\u003e1.6.1 Hazard Detection and Monitoring 15\u003c\/p\u003e \u003cp\u003e1.6.2 Early Warning Systems 16\u003c\/p\u003e \u003cp\u003e1.6.3 Risk Assessment and Vulnerability Mapping 16\u003c\/p\u003e \u003cp\u003e1.6.4 Environmental Monitoring 16\u003c\/p\u003e \u003cp\u003e1.6.5 Mapping and Damage Assessment 16\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 17\u003c\/p\u003e \u003cp\u003eAcknowledgments 17\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Role of Remote Sensing for Emergency Response and Disaster Rehabilitation 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMochamad Irwan Hariyono and Aptu Andy Kurniawan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Method 25\u003c\/p\u003e \u003cp\u003e2.3 Disaster Management 25\u003c\/p\u003e \u003cp\u003e2.4 Result and Discussion 26\u003c\/p\u003e \u003cp\u003e2.4.1 Floods 26\u003c\/p\u003e \u003cp\u003e2.4.2 Earthquakes 28\u003c\/p\u003e \u003cp\u003e2.4.3 Drought 29\u003c\/p\u003e \u003cp\u003e2.4.4 Landslides 29\u003c\/p\u003e \u003cp\u003e2.4.5 Land\/Forest Fire 30\u003c\/p\u003e \u003cp\u003e2.4.6 Volcanic Eruption 31\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 32\u003c\/p\u003e \u003cp\u003eReferences 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fundamentals of Disaster Management Using Remote Sensing 35\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGarima and Narayan Vyas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 35\u003c\/p\u003e \u003cp\u003e3.2 Importance of Remote Sensing in Disaster Management 36\u003c\/p\u003e \u003cp\u003e3.2.1 Role in Emergency Response 37\u003c\/p\u003e \u003cp\u003e3.2.2 Impact on Disaster Rehabilitation 38\u003c\/p\u003e \u003cp\u003e3.2.3 Remote Sensing Taxonomy 39\u003c\/p\u003e \u003cp\u003e3.3 Remote Sensing Applications in Emergency Response 40\u003c\/p\u003e \u003cp\u003e3.3.1 Damage Assessment 40\u003c\/p\u003e \u003cp\u003e3.3.1.1 Techniques and Methods 41\u003c\/p\u003e \u003cp\u003e3.3.1.2 Integration with Other Data Sources 42\u003c\/p\u003e \u003cp\u003e3.3.1.3 Feature Extraction from Pre- and Post- Disaster Imagery 43\u003c\/p\u003e \u003cp\u003e3.4 Acquisition of Disaster Features 45\u003c\/p\u003e \u003cp\u003e3.4.1 Acquisition of Tsunami Features with Remote Sensing 45\u003c\/p\u003e \u003cp\u003e3.4.2 Acquisition of Earthquake Features with Remote Sensing 48\u003c\/p\u003e \u003cp\u003e3.4.3 Acquisition of Wildfire Features with Remote Sensing 50\u003c\/p\u003e \u003cp\u003eConclusion 55\u003c\/p\u003e \u003cp\u003eReferences 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Remote Sensing for Monitoring of Disaster-Prone Region 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNavdeep Singh Sodhi and Sofia Singla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 60\u003c\/p\u003e \u003cp\u003e4.2 Related Existing Work 63\u003c\/p\u003e \u003cp\u003e4.3 Comparison Table 68\u003c\/p\u003e \u003cp\u003e4.4 Graphical Analysis 72\u003c\/p\u003e \u003cp\u003e4.5 Conclusion and Future Scope 74\u003c\/p\u003e \u003cp\u003eAcknowledgments 74\u003c\/p\u003e \u003cp\u003eReferences 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Artificial Intelligence Tools in Disaster Risk Reduction and Emergency Management 79\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRupinder Singh, Manjinder Singh and Jaswinder Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 80\u003c\/p\u003e \u003cp\u003e5.1.1 Role of AI Tools and Technologies 80\u003c\/p\u003e \u003cp\u003e5.1.2 Purpose and Objectives of the Research Paper 82\u003c\/p\u003e \u003cp\u003e5.2 AI Tools and Technologies in Disaster Risk Reduction 83\u003c\/p\u003e \u003cp\u003e5.3 Ethical and Social Implications of Using AI Tools in Disaster Management 91\u003c\/p\u003e \u003cp\u003e5.4 Impact and Effectiveness of AI Tools and Technologies 92\u003c\/p\u003e \u003cp\u003e5.5 AI for Dismantling Difficulties in Disaster Management 94\u003c\/p\u003e \u003cp\u003e5.6 Future Directions and Recommendations 95\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 95\u003c\/p\u003e \u003cp\u003eAcknowledgments 96\u003c\/p\u003e \u003cp\u003eFunding 96\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 AI Tools and Technologies in Disaster Risk Reduction and Management 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlisha Sinha and Laxmi Kant Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 100\u003c\/p\u003e \u003cp\u003e6.2 AI Tools in Different Phases of Disaster Management 101\u003c\/p\u003e \u003cp\u003e6.2.1 Before Disaster 101\u003c\/p\u003e \u003cp\u003e6.2.2 During Disaster 102\u003c\/p\u003e \u003cp\u003e6.2.3 After Disaster 102\u003c\/p\u003e \u003cp\u003e6.3 Use of Geospatial Technologies and AI in Disaster Management 103\u003c\/p\u003e \u003cp\u003e6.4 Future Challenges and Goals with AI 116\u003c\/p\u003e \u003cp\u003e6.5 Conclusions 116\u003c\/p\u003e \u003cp\u003eAcknowledgment 117\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 AI-Based Landslide Susceptibility Evaluation 125\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmanpreet Singh and Payal Kaushal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 126\u003c\/p\u003e \u003cp\u003e7.2 Principle of Support Vector Machines (SVM) 128\u003c\/p\u003e \u003cp\u003e7.3 Conclusion 132\u003c\/p\u003e \u003cp\u003eAcknowledgments 132\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Navigating Risk: A Comprehensive Study of Landslide Susceptibility Mapping and Hazard Assessment 139\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGaurav Kumar Saini and Inderdeep Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 140\u003c\/p\u003e \u003cp\u003e8.1.1 Challenges in Factor Selection and Weighting 141\u003c\/p\u003e \u003cp\u003e8.1.2 Combination of Subjective and Objective Approaches 141\u003c\/p\u003e \u003cp\u003e8.2 Factors Responsible for Landslides 141\u003c\/p\u003e \u003cp\u003e8.2.1 External 141\u003c\/p\u003e \u003cp\u003e8.2.2 Internal 142\u003c\/p\u003e \u003cp\u003e8.3 Types of Landslides 143\u003c\/p\u003e \u003cp\u003e8.4 Landslide Detection Techniques 144\u003c\/p\u003e \u003cp\u003e8.5 Landslide Monitoring Techniques 146\u003c\/p\u003e \u003cp\u003e8.6 Use of Machine Learning in Landslide Mapping 147\u003c\/p\u003e \u003cp\u003e8.7 Use of Deep Learning in Landslide Mapping 148\u003c\/p\u003e \u003cp\u003e8.8 Use of Ensemble Techniques 148\u003c\/p\u003e \u003cp\u003e8.9 Limitations of Existing Algorithms 149\u003c\/p\u003e \u003cp\u003e8.10 Dataset Used 149\u003c\/p\u003e \u003cp\u003e8.11 Model Architecture 153\u003c\/p\u003e \u003cp\u003e8.12 Results and Discussion 154\u003c\/p\u003e \u003cp\u003eAcknowledgment 157\u003c\/p\u003e \u003cp\u003eReferences 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Application of Geospatial Technology for Disaster Risk Reduction Using Machine Learning Algorithm and OpenStreetMap in Batticaloa District, Eastern Province, Sri Lanka 161\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZahir I.L.M., Suthakaran S., Iyoob A.L., Nuskiya M.H.F. and Fowzul Ameer M.L.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 162\u003c\/p\u003e \u003cp\u003e9.1.1 Geospatial Technology in DRR 163\u003c\/p\u003e \u003cp\u003e9.1.2 MLAs in DRR 164\u003c\/p\u003e \u003cp\u003e9.1.3 OSM in DRR 164\u003c\/p\u003e \u003cp\u003e9.1.4 Integrated Approach of Geospatial Technology, Machine Learning, and OSM 165\u003c\/p\u003e \u003cp\u003e9.2 Significance of the Study 165\u003c\/p\u003e \u003cp\u003e9.3 Objectives 167\u003c\/p\u003e \u003cp\u003e9.4 Methodology 167\u003c\/p\u003e \u003cp\u003e9.4.1 Study Area 167\u003c\/p\u003e \u003cp\u003e9.4.2 Data Collection 169\u003c\/p\u003e \u003cp\u003e9.4.2.1 MLAs for DRR 169\u003c\/p\u003e \u003cp\u003e9.4.2.2 Integration with OSM 171\u003c\/p\u003e \u003cp\u003e9.5 Results and Discussion 174\u003c\/p\u003e \u003cp\u003e9.6 Conclusion and Recommendations 179\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Landslide Displacement Forecasting With AI Models 185\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSangeetha Annam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 186\u003c\/p\u003e \u003cp\u003e10.1.1 Technology Classifications for Remote Sensing 187\u003c\/p\u003e \u003cp\u003e10.1.2 Architecture of Risk Management 189\u003c\/p\u003e \u003cp\u003e10.2 Artificial Intelligence-Based Forecasting of Landslide Displacement 191\u003c\/p\u003e \u003cp\u003e10.3 Performance Metrics 195\u003c\/p\u003e \u003cp\u003e10.4 Limitations in Assessing the AI Models for Landslide Displacement Prediction 196\u003c\/p\u003e \u003cp\u003e10.5 Technologies Integrated with AI Models 197\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 198\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Estimation of Snow Avalanche Hazardous Zones With AI Models 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajinder Kaur, Sartajvir Singh and Ganesh Kumar Sethi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 202\u003c\/p\u003e \u003cp\u003e11.2 Study Site and Data 203\u003c\/p\u003e \u003cp\u003e11.3 Methodology 204\u003c\/p\u003e \u003cp\u003e11.4 Results and Discussion 208\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 209\u003c\/p\u003e \u003cp\u003eReferences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Predicting and Understanding the Snow Avalanche Event 213\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNitin Arora and Sakshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 214\u003c\/p\u003e \u003cp\u003e12.2 Snow Avalanche 214\u003c\/p\u003e \u003cp\u003e12.2.1 Types of Snow Avalanche 216\u003c\/p\u003e \u003cp\u003e12.2.1.1 Sluff Avalanche 216\u003c\/p\u003e \u003cp\u003e12.2.1.2 Slab Avalanche 216\u003c\/p\u003e \u003cp\u003e12.2.2 Basic Reason Behind Snow Avalanche 217\u003c\/p\u003e \u003cp\u003e12.2.3 Role of Remote Sensing in Snow Avalanche Prediction 218\u003c\/p\u003e \u003cp\u003e12.3 Contributory Factors 219\u003c\/p\u003e \u003cp\u003e12.3.1 Terrain 220\u003c\/p\u003e \u003cp\u003e12.3.2 Precipitation 220\u003c\/p\u003e \u003cp\u003e12.3.2.1 Snow Accumulation 220\u003c\/p\u003e \u003cp\u003e12.3.2.2 Formation of Weak Layers 220\u003c\/p\u003e \u003cp\u003e12.3.2.3 Load and Stress Increases 220\u003c\/p\u003e \u003cp\u003e12.3.2.4 Rain-on-Snow Effect 220\u003c\/p\u003e \u003cp\u003e12.3.3 Wind Temperature 221\u003c\/p\u003e \u003cp\u003e12.3.4 Snowpack Stratigraphy 221\u003c\/p\u003e \u003cp\u003e12.4 Remote Sensing and Avalanche Prediction 221\u003c\/p\u003e \u003cp\u003e12.4.1 Basic Principle Behind Radar-Based Remote Sensing 222\u003c\/p\u003e \u003cp\u003e12.4.2 Need for Remote Sensing 223\u003c\/p\u003e \u003cp\u003e12.5 Methodology 223\u003c\/p\u003e \u003cp\u003e12.5 Conclusion and Future Scope 225\u003c\/p\u003e \u003cp\u003eReferences 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 A Systematic Review on Challenges and Opportunities in Snow Avalanche Risk Assessment and Analysis 229\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eApoorva Sharma, Bhavneet Kaur and Sartajvir Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 230\u003c\/p\u003e \u003cp\u003e13.2 Advanced Tools for Snow Avalanche Monitoring System 233\u003c\/p\u003e \u003cp\u003e13.3 Snow Avalanche Risk Assessment and Analysis 234\u003c\/p\u003e \u003cp\u003e13.4 Challenges in Snow Avalanche Risk Assessment and Analysis 237\u003c\/p\u003e \u003cp\u003e13.5 Opportunities in Snow Avalanche Risk Assessment and Analysis 237\u003c\/p\u003e \u003cp\u003e13.6 Summary 239\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 AI-Based Modeling of GLOF Process and Its Impact 243\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJaswinder Singh, Rajwinder Kaur, Puninder Kaur and Rupinder Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 244\u003c\/p\u003e \u003cp\u003e14.1.1 The Andes 245\u003c\/p\u003e \u003cp\u003e14.1.2 High Mountain Asia (HMA) 245\u003c\/p\u003e \u003cp\u003e14.1.3 Other Regions 245\u003c\/p\u003e \u003cp\u003e14.2 Artificial Intelligence and GLOF 246\u003c\/p\u003e \u003cp\u003e14.2.1 Modeling the GLOF Process 246\u003c\/p\u003e \u003cp\u003e14.2.2 Impact Assessment 246\u003c\/p\u003e \u003cp\u003e14.2.3 Benefits of Using AI 247\u003c\/p\u003e \u003cp\u003e14.2.4 AI Techniques for the Prediction of GLOF 247\u003c\/p\u003e \u003cp\u003e14.2.4.1 Machine Learning (ML) 248\u003c\/p\u003e \u003cp\u003e14.2.4.2 Deep Learning (DL) 248\u003c\/p\u003e \u003cp\u003e14.2.4.3 Time Series Analysis 248\u003c\/p\u003e \u003cp\u003e14.2.4.4 Integration with Other Techniques 249\u003c\/p\u003e \u003cp\u003e14.3 Machine Learning Techniques for GLOF 249\u003c\/p\u003e \u003cp\u003e14.3.1 Use of Supervised Learning in GLOF 249\u003c\/p\u003e \u003cp\u003e14.3.1.1 Data Preparation 249\u003c\/p\u003e \u003cp\u003e14.3.1.2 Feature Engineering 250\u003c\/p\u003e \u003cp\u003e14.3.1.3 Model Training 250\u003c\/p\u003e \u003cp\u003e14.3.1.4 Prediction 250\u003c\/p\u003e \u003cp\u003e14.3.1.5 Benefits of Using Supervised Learning for GLOF Prediction 250\u003c\/p\u003e \u003cp\u003e14.3.1.6 Various Supervised Algorithms for the GLOF Process 251\u003c\/p\u003e \u003cp\u003e14.3.1.7 Choosing the Right Algorithm 252\u003c\/p\u003e \u003cp\u003e14.3.2 Use of Unsupervised Learning in GLOF 253\u003c\/p\u003e \u003cp\u003e14.3.2.1 Anomaly Detection 253\u003c\/p\u003e \u003cp\u003e14.3.2.2 Feature Discovery 254\u003c\/p\u003e \u003cp\u003e14.3.2.3 Data Preprocessing 254\u003c\/p\u003e \u003cp\u003e14.3.2.4 Unsupervised Learning Algorithms for GLOF Analysis 255\u003c\/p\u003e \u003cp\u003e14.3.2.5 Choosing the Right Algorithm 256\u003c\/p\u003e \u003cp\u003e14.3.2.6 Objective 257\u003c\/p\u003e \u003cp\u003e14.3.2.7 Data Characteristics 257\u003c\/p\u003e \u003cp\u003e14.3.2.8 Benefits of Using Unsupervised Learning for GLOF 257\u003c\/p\u003e \u003cp\u003e14.3.2.9 Challenges and Considerations 257\u003c\/p\u003e \u003cp\u003e14.4 Deep Learning for GLOF Modeling 258\u003c\/p\u003e \u003cp\u003e14.4.1 Convolutional Neural Networks (CNNs) 258\u003c\/p\u003e \u003cp\u003e14.4.2 Recurrent Neural Networks (RNNs) 258\u003c\/p\u003e \u003cp\u003e14.4.3 Combining Different Deep Learning Techniques 259\u003c\/p\u003e \u003cp\u003e14.5 Existing Models for GLOF Modeling: A Comparison 260\u003c\/p\u003e \u003cp\u003e14.5.1 Statistical Models 260\u003c\/p\u003e \u003cp\u003e14.5.2 Machine Learning Models 261\u003c\/p\u003e \u003cp\u003e14.5.3 Deep Learning Models 261\u003c\/p\u003e \u003cp\u003e14.5.4 Comparison 262\u003c\/p\u003e \u003cp\u003e14.5.5 Choosing the Right Model 262\u003c\/p\u003e \u003cp\u003e14.5.6 Additional Considerations 262\u003c\/p\u003e \u003cp\u003e14.6 Future Models for GLOF Modeling 263\u003c\/p\u003e \u003cp\u003e14.6.1 Integration of Diverse Data Sources 263\u003c\/p\u003e \u003cp\u003e14.6.2 Explainable AI (XAI) 263\u003c\/p\u003e \u003cp\u003e14.6.3 Advanced Deep Learning Techniques 264\u003c\/p\u003e \u003cp\u003e14.6.4 Integration with Physical Modeling 264\u003c\/p\u003e \u003cp\u003e14.7 AI Challenges and Limitations 265\u003c\/p\u003e \u003cp\u003e14.8 Insights and Findings from AI-Based Modeling of GLOF Processes 265\u003c\/p\u003e \u003cp\u003e14.9 Evaluation of Methodology Used for AI-Based Modeling of GLOF Processes 266\u003c\/p\u003e \u003cp\u003e14.10 Conclusion 268\u003c\/p\u003e \u003cp\u003eReferences 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Systematic Review of the GLOF Susceptibility Assessment Techniques 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOushnik Banerjee, Anshu Kumari and Apoorva Shamra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 272\u003c\/p\u003e \u003cp\u003e15.2 Glacial Lakes in the Western Himalayas 273\u003c\/p\u003e \u003cp\u003e15.2.1 Gangotri Glacier (Supra Glacial Lake) 274\u003c\/p\u003e \u003cp\u003e15.2.2 Samudra Tapu (Pro Glacial Lake) 275\u003c\/p\u003e \u003cp\u003e15.2.3 South Lhonak Lake (Unconnected Glacial- Fed Lake) 275\u003c\/p\u003e \u003cp\u003e15.2.4 Dal Lake (Non-Glacial-Fed) 275\u003c\/p\u003e \u003cp\u003e15.3 Sensitive Glacial Lake in the Western Himalayas 276\u003c\/p\u003e \u003cp\u003e15.3.1 Samudra Tapu Glacier 276\u003c\/p\u003e \u003cp\u003e15.4 GLOF Susceptibility Mapping Techniques 277\u003c\/p\u003e \u003cp\u003e15.4.1 Satellite Imagery Analysis 277\u003c\/p\u003e \u003cp\u003e15.4.2 Semi-Automated GLOF Susceptibility Assessment System 278\u003c\/p\u003e \u003cp\u003e15.4.3 Glacial Lake Mapping 279\u003c\/p\u003e \u003cp\u003e15.5 Stages of Glaciations 279\u003c\/p\u003e \u003cp\u003e15.6 Glacier Retreat 281\u003c\/p\u003e \u003cp\u003e15.7 Causes of Glacial Lake Change 282\u003c\/p\u003e \u003cp\u003e15.8 Depiction and Categorization of Glacial Lakes 282\u003c\/p\u003e \u003cp\u003e15.9 Study of Evaluating Parameters 283\u003c\/p\u003e \u003cp\u003e15.9.1 Sensitivity Evaluation 283\u003c\/p\u003e \u003cp\u003e15.9.2 Calculation of Weights and GLOF Susceptibility Index 283\u003c\/p\u003e \u003cp\u003e15.10 Summary 284\u003c\/p\u003e \u003cp\u003eAcknowledgment 285\u003c\/p\u003e \u003cp\u003eReferences 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Challenges of GLOF Estimation and Prediction 289\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNeelam Dahiya, Sartajvir Singh and Puninder Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 290\u003c\/p\u003e \u003cp\u003e16.2 Types of GLOF 291\u003c\/p\u003e \u003cp\u003e16.2.1 Glacial Lakes 291\u003c\/p\u003e \u003cp\u003e16.2.2 Moraine-Dammed Lake 291\u003c\/p\u003e \u003cp\u003e16.2.3 Ice-Dammed Lakes 292\u003c\/p\u003e \u003cp\u003e16.3 Reasons for GLOF Occurrence 292\u003c\/p\u003e \u003cp\u003e16.3.1 Glacial Retreat 292\u003c\/p\u003e \u003cp\u003e16.3.2 Geothermal Activity 293\u003c\/p\u003e \u003cp\u003e16.3.3 Avalanches 293\u003c\/p\u003e \u003cp\u003e16.3.4 Earthquakes and Landslides 294\u003c\/p\u003e \u003cp\u003e16.3.5 Human Activities 294\u003c\/p\u003e \u003cp\u003e16.3.6 Glacial Moraine Failure 295\u003c\/p\u003e \u003cp\u003e16.3.7 Glacier Lake Expansion 295\u003c\/p\u003e \u003cp\u003e16.3.8 Glacier Surging and Calving 295\u003c\/p\u003e \u003cp\u003e16.4 Challenges Faced for GLOF Estimation 296\u003c\/p\u003e \u003cp\u003e16.4.1 Early Detection 296\u003c\/p\u003e \u003cp\u003e16.4.2 Infrastructure Damage 297\u003c\/p\u003e \u003cp\u003e16.4.3 Loss of Life 297\u003c\/p\u003e \u003cp\u003e16.4.4 Economic Impact 298\u003c\/p\u003e \u003cp\u003e16.4.5 Environmental Degradation 298\u003c\/p\u003e \u003cp\u003e16.4.6 Climate Changes 299\u003c\/p\u003e \u003cp\u003e16.5 GLOF Solution 299\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 299\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Real-Time Earthquake Monitoring with Remote Sensing and AI Technology 303\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKoushik Sundar, Narayan Vyas and Neha Bhati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 304\u003c\/p\u003e \u003cp\u003e17.2 Basics of AI and Remote Sensing 305\u003c\/p\u003e \u003cp\u003e17.2.1 AI Applications in Earthquake Monitoring 306\u003c\/p\u003e \u003cp\u003e17.2.1.1 Optical Remote Sensing 306\u003c\/p\u003e \u003cp\u003e17.2.1.2 Microwave Remote Sensing 307\u003c\/p\u003e \u003cp\u003e17.2.2 Satellites and Sensors 308\u003c\/p\u003e \u003cp\u003e17.2.3 AI and Remote Sensing for Integration in Monitoring Earthquakes 308\u003c\/p\u003e \u003cp\u003e17.2.4 Challenges and Future Directions 310\u003c\/p\u003e \u003cp\u003e17.3 Advances in Satellite Remote Sensing Techniques for Improved Earthquake Monitoring 310\u003c\/p\u003e \u003cp\u003e17.3.1 Comparative Analysis of Remote Sensing Satellites 310\u003c\/p\u003e \u003cp\u003e17.3.2 Comparison of Optical and Microwave Satellite Imagery 311\u003c\/p\u003e \u003cp\u003e17.3.3 Case Study on Pre- and Post-images of Earthquake in Doti District of Nepal 313\u003c\/p\u003e \u003cp\u003e17.4 How AI Is Currently Being Used in Remote Sensing to Monitor Earthquakes 315\u003c\/p\u003e \u003cp\u003e17.4.1 Automated Image Processing 315\u003c\/p\u003e \u003cp\u003e17.4.2 Seismic Data Augmentation 316\u003c\/p\u003e \u003cp\u003e17.4.3 Risk Assessment and Management 316\u003c\/p\u003e \u003cp\u003e17.4.4 Integrated Monitoring Systems 317\u003c\/p\u003e \u003cp\u003e17.5 Ongoing and Future Practical AI Applications in Remote Sensing 318\u003c\/p\u003e \u003cp\u003e17.5.1 More Sophisticated Prediction Models 318\u003c\/p\u003e \u003cp\u003e17.5.2 Real-Time Data Processing 318\u003c\/p\u003e \u003cp\u003e17.5.3 Damage and Recovery 319\u003c\/p\u003e \u003cp\u003e17.5.4 Public Safety and Community Resilience 319\u003c\/p\u003e \u003cp\u003e17.6 Conclusion 320\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Enhancing Seismic-Events Identification and Analysis Using Machine Learning Approach 323\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGurwinder Singh, Harun and Tejinder Pal Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 324\u003c\/p\u003e \u003cp\u003e18.2 Methodology 326\u003c\/p\u003e \u003cp\u003e18.3 Results and Discussion 329\u003c\/p\u003e \u003cp\u003e18.3.1 ml Models 333\u003c\/p\u003e \u003cp\u003e18.3.2 ARIMA Models 334\u003c\/p\u003e \u003cp\u003e18.3.3 Neural Network Models 335\u003c\/p\u003e \u003cp\u003e18.3.4 Spatial Analysis 338\u003c\/p\u003e \u003cp\u003e18.4 Limitations 340\u003c\/p\u003e \u003cp\u003e18.5 Future Directions 340\u003c\/p\u003e \u003cp\u003e18.6 Conclusion and Future Scope 341\u003c\/p\u003e \u003cp\u003eReferences 341\u003c\/p\u003e \u003cp\u003eIndex 343\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand 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