This volume presents a transformative vision for water resource management in an era of climate uncertainty, bridging millennia-old indigenous wisdom with cutting-edge technologies. Climate-Smart Water Resource Management: A Paradigm Shift from Ancient to Modern Practices with Integrated Technologies brings together sixteen chapters that span foundational concepts, artificial intelligence and machine learning frontiers, geospatial and remote sensing applications, and a forward-looking synthesis. From the time-tested bamboo irrigation networks and paddy-cum-fish cultivation of the Apatani tribe in the Indian Himalayas to the use of Sentinel‑1 SAR for flood inundation mapping, from entropy-based precipitation complexity analysis to bidirectional LSTM networks for streamflow forecasting, and from Large Language Models reimagining stakeholder engagement to LiDAR-enabled precision topography – this volume demonstrates how the integration of traditional ecological knowledge with AI‑ML, RS‑GIS, and LLMs can forge climate-resilient, equitable, and sustainable water futures. Designed for researchers, policymakers, practitioners, and students, the book provides both theoretical depth and implementable strategies, culminating in a call for interdisciplinary collaboration and policy innovation. It is an essential compendium for anyone committed to reimagining how humanity governs its most precious resource in a rapidly changing world.
1. Chapter One: Introduction to climate-smart water resource management with AI-ML, RS-GIS, and LLMs - p. 3 2. Chapter Two: Indigenous water resource management systems of the Apatani tribe: Bridging traditional practices with modern solutions in the Indian Himalayan Region - p. 23 3. Chapter Three: River water quality assessment and management using water quality index - p. 49 4. Chapter Four: Assessing spatiotemporal variability of precipitation complexity in Brahmani River Basin using entropy indices - p. 69 5. Section B: Artificial intelligence and machine learning frontiers: From forecasting to large language models - p. S91 6. Chapter Five: Enhancing seasonal rainfall forecasting using machine learning approaches - p. 93 7. Chapter Six: Enhancing water quality assessment through machine learning: Exploring machine learning applications for accurate water quality assessment - p. 107 8. Chapter Seven: Exploring the efficacy of different variants of long short-term memory networks in daily streamflow forecasting - p. 131 9. Chapter Eight: Integrating large language models (LLMs) in climate-smart water management: Future approaches and innovations - p. 149 10. Section C: Geospatial technologies and remote sensing: Precision tools for water resources management - p. S191 11. Chapter Nine: Prioritizing and assessing the surface water quality through geospatial techniques - p. 193 12. Chapter Ten: Harnessing the potential of Sentinel-1 SAR data for flood inundation mapping - p. 221 13. Chapter Eleven: Prioritizing sub-watersheds for soil conservation in the Dikhu watershed, North-East India using morphometric and LULC analysis - p. 241 14. Chapter Twelve: Assessing the impact of long-term rainfall and temperature variability on land use and land cover - p. 267 15. Chapter Thirteen: Determination of reservoir water quality parameters in a sugar cane plantation using remote sensing algorithms - p. 291 16. Chapter Fourteen: Advancements in LiDAR technology for precision water resource management - p. 317 17. Chapter Fifteen: Integrating remote sensing and machine learning in Indian aquaculture water management: Lessons from two decades - p. 339 18. Section D: Synthesis and future directions - p. S361 19. Chapter Sixteen: Synthesizing climate-smart water resource management with recently developed technologies (AI-ML, RS-GIS, and LLMs) - p. 363
This volume presents a transformative vision for water resource management in an era of climate uncertainty, bridging millennia-old indigenous wisdom with cutting-edge technologies. Climate-Smart Water Resource Management: A Paradigm Shift from Ancient to Modern Practices with Integrated Technologies brings together sixteen chapters that span foundational concepts, artificial intelligence and machine learning frontiers, geospatial and remote sensing applications, and a forward-looking synthesis. From the time-tested bamboo irrigation networks and paddy-cum-fish cultivation of the Apatani tribe in the Indian Himalayas to the use of Sentinel‑1 SAR for flood inundation mapping, from entropy-based precipitation complexity analysis to bidirectional LSTM networks for streamflow forecasting, and from Large Language Models reimagining stakeholder engagement to LiDAR-enabled precision topography – this volume demonstrates how the integration of traditional ecological knowledge with AI‑ML, RS‑GIS, and LLMs can forge climate-resilient, equitable, and sustainable water futures. Designed for researchers, policymakers, practitioners, and students, the book provides both theoretical depth and implementable strategies, culminating in a call for interdisciplinary collaboration and policy innovation. It is an essential compendium for anyone committed to reimagining how humanity governs its most precious resource in a rapidly changing world.
1. Chapter One: Introduction to climate-smart water resource management with AI-ML, RS-GIS, and LLMs - p. 3 2. Chapter Two: Indigenous water resource management systems of the Apatani tribe: Bridging traditional practices with modern solutions in the Indian Himalayan Region - p. 23 3. Chapter Three: River water quality assessment and management using water quality index - p. 49 4. Chapter Four: Assessing spatiotemporal variability of precipitation complexity in Brahmani River Basin using entropy indices - p. 69 5. Section B: Artificial intelligence and machine learning frontiers: From forecasting to large language models - p. S91 6. Chapter Five: Enhancing seasonal rainfall forecasting using machine learning approaches - p. 93 7. Chapter Six: Enhancing water quality assessment through machine learning: Exploring machine learning applications for accurate water quality assessment - p. 107 8. Chapter Seven: Exploring the efficacy of different variants of long short-term memory networks in daily streamflow forecasting - p. 131 9. Chapter Eight: Integrating large language models (LLMs) in climate-smart water management: Future approaches and innovations - p. 149 10. Section C: Geospatial technologies and remote sensing: Precision tools for water resources management - p. S191 11. Chapter Nine: Prioritizing and assessing the surface water quality through geospatial techniques - p. 193 12. Chapter Ten: Harnessing the potential of Sentinel-1 SAR data for flood inundation mapping - p. 221 13. Chapter Eleven: Prioritizing sub-watersheds for soil conservation in the Dikhu watershed, North-East India using morphometric and LULC analysis - p. 241 14. Chapter Twelve: Assessing the impact of long-term rainfall and temperature variability on land use and land cover - p. 267 15. Chapter Thirteen: Determination of reservoir water quality parameters in a sugar cane plantation using remote sensing algorithms - p. 291 16. Chapter Fourteen: Advancements in LiDAR technology for precision water resource management - p. 317 17. Chapter Fifteen: Integrating remote sensing and machine learning in Indian aquaculture water management: Lessons from two decades - p. 339 18. Section D: Synthesis and future directions - p. S361 19. Chapter Sixteen: Synthesizing climate-smart water resource management with recently developed technologies (AI-ML, RS-GIS, and LLMs) - p. 363
