Hydrological Insights
Synergizing Groundwater Models, Remote Sensing, and AI for Water Sustainability
- 1st Edition - November 1, 2025
- Imprint: Elsevier
- Editors: Hossein Hashemi, Amit Kumar, Krishna Kumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 6 3 9 4 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 6 3 9 5 - 5
Hydrological Insights: Synergizing Groundwater Models, Remote Sensing, and AI for Water Sustainability offers an in-depth exploration of hydrological modeling and its cuttin… Read more
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Request a sales quoteHydrological 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. In Part IV, the focus shifts to 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.
- Covers the latest developments and future trends in AI and machine-learning in the fields of hydrology and water resource engineering
- Includes advanced groundwater methods and techniques demonstrated through real-world case studies with step-by-step analysis
- Offers an easy-to-follow structure with data-driven approaches and is authored by world acclaimed experts in the fields of hydrology and AI
Graduate and postgraduate students learning about hydrology/groundwater and AI-methods.
Part I: Foundations of Hydrological Modeling
1. Introduction to Data-Driven Groundwater Modeling: Methods, Applications & Challenges
2. InSAR-Based Estimation of Head and Storage Changes: Numerical Models and Data Driven Techniques
3. Surfacewater Flow as a Mitigation Measure for Land Subsidence Mitigation in Rural and Urban Areas
4. Hydro-Meteorological Droughts: Patterns, Trends, and the Role of Accumulation Periods on Groundwater Condition
Part II: Advanced Techniques in Hydrological Studies
5. Automated Hydrological Variable Estimation: Novel Approaches and Optimization Algorithms
6. Spatiotemporal Variability of Hydrometeorological Parametrs: Insights from River Basin Analysis
7. Monitoring Carbon Exchange in Wetlands and Peatlands Using InSAR-Based Methods
8. Impact of Drinking and Sanitary Water Separation on Drinking Water Quality: Groundwater Quality Mapping
Part III: Integration of Remote Sensing and Artificial Intelligence in Hydrology
9. InSAR-AI-Based Approach for Groundwater Level Prediction in Arid Regions
10. Spatiotemporal Variation of Environmental Hazards: Remote Sensing and AI Applications
11. Detecting Changes in Global Satellite-Based Hydrological Observations using AI Techniques
12. Satellite Monitoring of Infrastructure using Interferometric Synthetic Aperture Radar (InSAR)
Part IV: Environmental Impact Assessment and Management Strategies
13. Quantitative and Qualitative Assessment of Streamflow Variation: Climate vs. Human Impact
14. Assessing Contaminated Groundwater Sites in Industrial Areas with Limited Data Availability
15. Flood Spreading Project Suitability Mapping: Water Resources Management using Machine Learning Algorithms
Part V: Advances in Remote Sensing and Machine Learning
16. Advanced Machine Learning Algorithms for Assessing Groundwater Potential using Remote Sensing-Derived Data
17. Extreme Gradient Boosting and Random Forest Algorithms for Assessing Groundwater Spring Potential using DEM-Derived Factors
18. Remote Sensing Techniques and Machine Learning Algorithms in Groundwater Vulnerability Mapping
Part VI: Case Studies and Future Directions
19. Evaluation of Weather Radar Systems for Operational Use in Hydrological Studies
20. Towards Intelligent Assessment of Groundwater Resources: Trends, Challenges, and Future Directions
1. Introduction to Data-Driven Groundwater Modeling: Methods, Applications & Challenges
2. InSAR-Based Estimation of Head and Storage Changes: Numerical Models and Data Driven Techniques
3. Surfacewater Flow as a Mitigation Measure for Land Subsidence Mitigation in Rural and Urban Areas
4. Hydro-Meteorological Droughts: Patterns, Trends, and the Role of Accumulation Periods on Groundwater Condition
Part II: Advanced Techniques in Hydrological Studies
5. Automated Hydrological Variable Estimation: Novel Approaches and Optimization Algorithms
6. Spatiotemporal Variability of Hydrometeorological Parametrs: Insights from River Basin Analysis
7. Monitoring Carbon Exchange in Wetlands and Peatlands Using InSAR-Based Methods
8. Impact of Drinking and Sanitary Water Separation on Drinking Water Quality: Groundwater Quality Mapping
Part III: Integration of Remote Sensing and Artificial Intelligence in Hydrology
9. InSAR-AI-Based Approach for Groundwater Level Prediction in Arid Regions
10. Spatiotemporal Variation of Environmental Hazards: Remote Sensing and AI Applications
11. Detecting Changes in Global Satellite-Based Hydrological Observations using AI Techniques
12. Satellite Monitoring of Infrastructure using Interferometric Synthetic Aperture Radar (InSAR)
Part IV: Environmental Impact Assessment and Management Strategies
13. Quantitative and Qualitative Assessment of Streamflow Variation: Climate vs. Human Impact
14. Assessing Contaminated Groundwater Sites in Industrial Areas with Limited Data Availability
15. Flood Spreading Project Suitability Mapping: Water Resources Management using Machine Learning Algorithms
Part V: Advances in Remote Sensing and Machine Learning
16. Advanced Machine Learning Algorithms for Assessing Groundwater Potential using Remote Sensing-Derived Data
17. Extreme Gradient Boosting and Random Forest Algorithms for Assessing Groundwater Spring Potential using DEM-Derived Factors
18. Remote Sensing Techniques and Machine Learning Algorithms in Groundwater Vulnerability Mapping
Part VI: Case Studies and Future Directions
19. Evaluation of Weather Radar Systems for Operational Use in Hydrological Studies
20. Towards Intelligent Assessment of Groundwater Resources: Trends, Challenges, and Future Directions
- Edition: 1
- Published: November 1, 2025
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443363948
- eBook ISBN: 9780443363955
HH
Hossein Hashemi
Hossein Hashemi is an associate professor (universitetslektor) at the Department of Water Resources Engineering and Center for Advanced Middle Eastern Studies at Lund University. He obtained his Ph.D. in Water Resources Engineering, where he specialized in groundwater management, water harvesting techniques, and groundwater artificial recharge. These techniques are essential in ensuring the availability of water supply and reaching sustainable water resources use in arid regions. Before joining Lund University, he served as a research engineer at the Center for Agriculture and Natural Resources in Iran for several years.
After Ph.D., he worked as a postdoctoral scholar at the Center for Groundwater Evaluation and Management (GEM), Department of Geophysics at Stanford University, USA, between 2015 and 2017. During his time at Stanford, he focused on the application of remote sensing techniques in the field of hydrology and groundwater hydrology. This included studying various aspects such as precipitation, groundwater, and subsidence.
Hossein's research interests mainly revolve around remote sensing, artificial intelligence, climate change, and surface water and groundwater management.Affiliations and expertise
Associate Professor, Department of Water Resources Engineering and Center for Advanced Middle Eastern Studies, Lund University, SwedenAK
Amit Kumar
Dr. Amit Kumar is a Professor of Hydrology and Water Resources at Nanjing University of Information Science and Technology. He obtained his M.Tech and PhD from the Indian Institute of Technology (IIT) Roorkee and conducted his postdoctoral research at Hohai University, China. Dr. Kumar’s research spans the ecological health of aquatic habitats, water quality modeling, and water security practices to meet the Sustainable Development Goals. He has published five books, including Elsevier’s Climate Change in the Himalayas, and is an advisory board member for the Elsevier journal Ecological Indicators.
Affiliations and expertise
Nanjing University of Information Science and Technology, School of Hydrology and Water Resources, Nanjing, ChinaKK
Krishna Kumar
Dr. Krishna Kumar (Member, IEEE) has more than 14 years of experience in Renewable Energy Development. He is presently a researcher at the Department of Water Resources Engineering and Center for Advanced Middle Eastern Studies at Lund University, Sweden. He holds a PhD in Renewable Energy. He worked as a postdoctoral research fellow at the School of Electrical and Electronics Engineering in the Rolls-Royce Corporate Lab, Nanyang Technological University, Singapore, from February 2023 to March 2024. Prior to that, he worked as a Senior Engineer at UJVN Ltd. (A State Government PSU of Uttarakhand (India)) from January 2013 to February 2023. His expertise includes the design, installation, commissioning, testing, and operation & maintenance of hydro, solar, and wind power plants. Dr. Kumar has also served as an Assistant Professor/Lecturer on the term appointment basis at Kumaon Engineering College, Dwarahat, Uttarakhand (India); Bharat Institute of Technology, Meerut (India); and Uttaranchal Institute of Technology, Dehradun (India), for more than three years. Dr. Kumar has published numerous highly cited SCI/SCIE research articles in reputed journals. He has also authored/edited 12 books with publishers such as Elsevier, Wiley, Taylor & Francis, Springer, and River Press. His current research interests include Renewable Energy, Multiphase Machines, Electrical Equipment Health Monitoring Systems, AI/ML, and data science.
Affiliations and expertise
Researcher, Department of Water Resources Engineering and Center for Advanced Middle Eastern Studies, Lund University, Sweden