Land and Water Resources Management Using Machine Learning and Geospatial Techniques
- 1st Edition - June 1, 2026
- Latest edition
- Editors: Mahesh Chand Singh, Anurag Malik, Santosh Subhash Palmate, Mehdi Jamei, Sonam Sandeep Dash
- Language: English
Land and Water Resources Management Using Machine Learning and Geospatial Techniques addresses critical knowledge gaps in hydrology, remote sensing, and soil and water conser… Read more
- Equips professionals with the latest tools and insights necessary for addressing the contemporary challenges associated with land and water management
- Provides practical solutions and remedial measures for effective soil erosion control
- Explores the fusion of geospatial tools with ML-based models
1. Introduction to basic watershed hydrology governing soil erosion.
2. Introduction to land and water management using geospatial techniques.
3. Geospatial techniques for soil erosion assessment and sediment transport.
4. Geospatial techniques for land degradation and reservoir sedimentation assessment.
5. Introduction to different technologies/remedial measures for controlling soil erosion/loss.
Section B: Modelling Approaches for Soil Loss Estimation
6. Application of SWAT model for soil loss prediction and risk assessment.
7. Application of the WEPP model for soil loss prediction and risk assessment.
8. Application of USLE, RUSLE and MUSLE for soil loss prediction and risk assessment.
9. Application of AI in soil and water conservation planning and management.
10. Application of any other soil erosion/loss prediction and risk assessment model.
Section C: Machine Learning Approaches for Soil Loss Prediction
11. AI-based models for erosion estimation and soil loss prediction.
12. AI-based models for simulating rainfall-runoff process.
13. AI-based models for stream-flow forecasting.
14. Machine learning models for sediment-load prediction and reservoir operations.
Section D: Hybrid Applications
15. Integrated use of geospatial techniques with machine learning models for spatial erosion prediction.
16. Integration of GIS with physically based models for soil loss prediction and watershed prioritization.
17. Integration of numerical and empirical models with geospatial techniques for erosion and sediment yield prediction.
18. Integrated applications of geospatial techniques and machine learning models for reservoir sedimentation.
19. Integration of SWAT model with GIS for soil loss/sediment yield prediction.
20. Advanced techniques of soil erosion management/control.
- Edition: 1
- Latest edition
- Published: June 1, 2026
- Language: English
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Mahesh Chand Singh
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Anurag Malik
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Santosh Subhash Palmate
Dr. Santosh Subhash Palmate is an assistant professor of arid hydrology and water systems in the Texas A&M AgriLife Research, El Paso Center, and the Department of Biological and Agricultural Engineering at Texas A&M University, USA. He has experience in hydrological modeling, agroecosystem modeling, participatory modeling, land use/cover and climate change impact assessment, and best management practices evaluation. Dr. Palmate is an early-career scientist working on the sustainable management of water resources in arid and semi-arid regions of the globe. He supports the development of resilient water practices with a special emphasis on dynamics in the regional water and other resources in the inter-state and transboundary areas, utilizing advanced remote sensing (RS) and geographical information systems (GIS) to understand the linkages and feedback mechanisms between water, climate, agriculture, urban, stakeholders, and socio-economy.
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Mehdi Jamei
Dr Mehdi has been an Assistant Professor at the Shahid Chamran University of Ahvaz, Ahaz, Iran, since 2017 and has been a Postdoctoral Research Associate at the University of Prince Edward Island since 2023. He received his Bachelor's degree in Civil engineering from Ahvaz University in 2003 and Master's and Ph.D. degrees in hydraulic structure and water resource management d in 2005 and 2015, respectively, from the Shahid Chamran University of Ahvaz. His research interest is modelling computational fluid dynamics, finite element method, discontinuous Galerkin method, control volume, Hydro carbonate reservoir simulation, porous media, soft computing, and hand hydrological processes, including rainfall-runoff relationship, suspended sediment load, evaporation, renewable energy, evapotranspiration, soil temperature, meteorological and hydrological droughts using artificial intelligence techniques, trend analysis, watershed planning, and management, groundwater quality analysis, drip irrigation system, Nanofluid thermo-physical properties, sustainable water engineering. He is also Guest editor of the Water journal of MDPI since April 2023. He has published 90 international and 5 national research papers in reputed journals and 2 book chapters with an H-index of 26 and a total of 1575 citations on ResearchGate (https://www.researchgate.net/profile/Mehdi-Jamei?ev=hdr_xprf). and 2692 citations GoogleScholar(https://scholar.google.com/citations?hl=en&user=cg9YfZ0AAAAJ&view_op=list_works&sortby=pubdate). He is also serving as a reviewer for more than 68 International journals. Dr. Mehdi also received the top faculty member in 2021, 2022, and 2023 at the Shahid Chamran University of Ahvaz. Also, He worked for 12 years as a signor engineer and a project manager in famous consulting companies in Iran.
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