Hydrological Insights: Synergizing Groundwater Models, Remote Sensing, and AI for Water Sustainability offers an in-depth exploration of hydrological modeling and its cutting-edge advancements, presented across six comprehensive sections. Part I establishes the foundational principles and methodologies of hydrological modeling, while Part II delves into sophisticated techniques and tools that enhance the accuracy and efficiency of hydrological studies. Part III highlights the powerful integration of remote sensing and artificial intelligence, showcasing how these technologies revolutionize modern hydrological practices.
Part IV focuses on environmental impact assessment and management strategies, outlining effective methods for sustainable water resource management. Part V covers the latest advancements in remote sensing and machine learning, emphasizing their pivotal role in contemporary hydrology. Finally, Part VI presents real-world case studies and future directions, offering practical insights and forward-looking perspectives. With meticulously crafted chapters that combine theoretical foundations with practical applications, this book is an essential resource for students, researchers, and professionals seeking to advance their understanding of hydrology through the integration of remote sensing and AI.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of contributors - p. xv 8. Preface - p. xvii 9. Chapter 1. Data-driven approaches to groundwater modelling: methods, applications, and challenges - p. 1 10. Chapter 2. Analysis of changes in the hydrological response of the gangotri glacier, Garhwal Himalaya - p. 11 11. Chapter 3. An advanced approach for assessment of spatiotemporal variation in rainfall and its impact on stream flow discharge in Chaliyar River Basin of Kerala - p. 21 12. Chapter 4. Groundwater depletion in a changing climate: global insights and strategic mitigation approaches - p. 35 13. Chapter 5. Application of machine learning/artificial intelligence and IoT in water resources modeling, management, and mitigation - p. 59 14. Chapter 6. Kernel-based predictive framework for groundwater level forecast in semiarid areas: a CEEMDAN-based GWO-KELM hybrid mode - p. 85 15. Chapter 7. Interferometric synthetic aperture radar-artificial intelligence-based approach for groundwater level prediction in arid regions - p. 99 16. Chapter 8. Technology for hydro-ecology and big data for smart water management - p. 111 17. Chapter 9. Bridging the gap: geospatial technology, artificial intelligence, and hydrological models for integrated water resource management - p. 149 18. Chapter 10. The cost-benefit analysis of automation in water quality management - p. 173 19. Chapter 11. Human negligence on H₂O—unseen impact on life and infrastructure with preventive measures, including intellectual property rights - p. 189 20. Chapter 12. Innovative approaches to groundwater contamination: remote sensing and artificial intelligence in the Midlands Industrial Belt - p. 203 21. Chapter 13. Shared waters, shared futures: toward sustainable groundwater management in Cyprus - p. 227 22. Chapter 14. Transboundary aquifer cooperation: supranational coordination in managing the North-Western Sahara Aquifer System - p. 237 23. Chapter 15. Glacier mass balance in the Indian Himalayan Region: impacts on water resources, hazards, and mitigation - p. 249 24. Chapter 16. Enhanced remote sensing of water surface elevation through fusion of Sentinel-3 altimeter data and climate variables using machine learning - p. 261 25. Index - p. 273
Hydrological Insights: Synergizing Groundwater Models, Remote Sensing, and AI for Water Sustainability offers an in-depth exploration of hydrological modeling and its cutting-edge advancements, presented across six comprehensive sections. Part I establishes the foundational principles and methodologies of hydrological modeling, while Part II delves into sophisticated techniques and tools that enhance the accuracy and efficiency of hydrological studies. Part III highlights the powerful integration of remote sensing and artificial intelligence, showcasing how these technologies revolutionize modern hydrological practices.
Part IV focuses on environmental impact assessment and management strategies, outlining effective methods for sustainable water resource management. Part V covers the latest advancements in remote sensing and machine learning, emphasizing their pivotal role in contemporary hydrology. Finally, Part VI presents real-world case studies and future directions, offering practical insights and forward-looking perspectives. With meticulously crafted chapters that combine theoretical foundations with practical applications, this book is an essential resource for students, researchers, and professionals seeking to advance their understanding of hydrology through the integration of remote sensing and AI.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of contributors - p. xv 8. Preface - p. xvii 9. Chapter 1. Data-driven approaches to groundwater modelling: methods, applications, and challenges - p. 1 10. Chapter 2. Analysis of changes in the hydrological response of the gangotri glacier, Garhwal Himalaya - p. 11 11. Chapter 3. An advanced approach for assessment of spatiotemporal variation in rainfall and its impact on stream flow discharge in Chaliyar River Basin of Kerala - p. 21 12. Chapter 4. Groundwater depletion in a changing climate: global insights and strategic mitigation approaches - p. 35 13. Chapter 5. Application of machine learning/artificial intelligence and IoT in water resources modeling, management, and mitigation - p. 59 14. Chapter 6. Kernel-based predictive framework for groundwater level forecast in semiarid areas: a CEEMDAN-based GWO-KELM hybrid mode - p. 85 15. Chapter 7. Interferometric synthetic aperture radar-artificial intelligence-based approach for groundwater level prediction in arid regions - p. 99 16. Chapter 8. Technology for hydro-ecology and big data for smart water management - p. 111 17. Chapter 9. Bridging the gap: geospatial technology, artificial intelligence, and hydrological models for integrated water resource management - p. 149 18. Chapter 10. The cost-benefit analysis of automation in water quality management - p. 173 19. Chapter 11. Human negligence on H₂O—unseen impact on life and infrastructure with preventive measures, including intellectual property rights - p. 189 20. Chapter 12. Innovative approaches to groundwater contamination: remote sensing and artificial intelligence in the Midlands Industrial Belt - p. 203 21. Chapter 13. Shared waters, shared futures: toward sustainable groundwater management in Cyprus - p. 227 22. Chapter 14. Transboundary aquifer cooperation: supranational coordination in managing the North-Western Sahara Aquifer System - p. 237 23. Chapter 15. Glacier mass balance in the Indian Himalayan Region: impacts on water resources, hazards, and mitigation - p. 249 24. Chapter 16. Enhanced remote sensing of water surface elevation through fusion of Sentinel-3 altimeter data and climate variables using machine learning - p. 261 25. Index - p. 273
