Land and Water Resources Management Using Machine Learning and Geospatial Techniques
Will be published approx. on 1. February 2027
Book
Paperback/Softback
400 pages
978-0-443-36306-1 (ISBN)
Description
Land and Water Resources Management Using Machine Learning and Geospatial Techniques addresses critical knowledge gaps in hydrology, remote sensing, and soil and water conservation. The book explores various methodologies for estimating soil loss, encompassing modeling techniques, geospatial methodologies, and machine learning approaches in an effort to empower researchers in their pursuit of sustainable solutions for effective land and water management. Furthermore, it explore the fusion of geospatial tools with ML-based models, fostering an innovative approach to resource management.
More details
Series
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
ISBN-13
978-0-443-36306-1 (9780443363061)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Dr. Mahesh Chand Singh holds a PhD degree and has a background in Soil and Water Engineering. Early in his career, he worked as an Assistant Professor at Mata Gujri College, Fatehgarh Sahib, Punjab, India. He has held positions at various institutions, including Dr. Y. S. Parmar University of Horticulture and Forestry in Solan, Himachal Pradesh, where he was appointed as a scientist in the Department of Soil Science and Water Management. He has also worked as a research associate at NASF, ARSB, New Delhi. He gained international experience by working as a Research Associate with IRRI, Philippines, under an IFAD-funded research project. Further, Dr. Singh has worked on water conservation techniques, microirrigation and irrigation scheduling. He has developed several mathematical models for predicting greenhouse microclimate under soilless cropped conditions. As of the most recent information available, Dr. Singh is a Scientist in the Department of Soil and Water Engineering at the College of Agricultural Engineering and Technology of Punjab Agricultural University (PAU) in Ludhiana, Punjab, India. He is actively involved in teaching at both undergraduate and postgraduate levels. Dr. Singh's research interests span various areas, including irrigation and drainage systems, remote sensing, GIS applications, and protected cultivation. He has been involved in several research projects related to these topics. He has made significant contributions to the field with over seventy research papers published in internationally reputed journals, four book chapters, four popular articles, and one practical manual. He has received awards such as the Best Scientist Award and PAU Gold Medal for Doctoral Research in recognition of his achievements. His work on "Soilless Media Cultivation Practices for Seedless (Parthenocarpic) Cucumber? has been included in the package of practices of PAU. He has also participated as a reviewer for various scientific journals, including Archives of Agronomy and Soil Science, Scientia Horticulturae, and Journal of the Science of Food and Agriculture (Wiley). He is a life member of the Indian Society of Agricultural Engineers (ISAE), which indicates his active involvement in the professional community. Dr. Mahesh Chand Singh's career showcases a commitment to advancing agricultural engineering and soil science through research, teaching, and collaboration with various national and international institutions. His contributions have been recognized through awards, publications, and active participation in academic and professional activities. Dr. Anurag Malik has been serving at Punjab Agricultural University, Regional Research Station, Bathinda, as a Scientist (Agril. Engg.) since 2019. He received his Bachelor's degree (Agril. Engg.) from Aligarh College of Engineering and Technology, Aligarh, in 2011, and Master's and Ph.D. degrees in Soil and Water Conservation Engg. from G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand during 2014 and 2019, respectively. His research interest is modelling hydrological processes, including rainfall-runoff relationship, suspended sediment load, evaporation, evapotranspiration, soil temperature, meteorological and hydrological droughts using artificial intelligence techniques, trend analysis, watershed planning and management, watershed prioritization, groundwater quality analysis, drip irrigation system, and crop simulation. He is also working as a co-principal investigator in the project on Centre of Excellence for Utilization of "Brackish Water for Fruits and Vegetables Production in South-West Punjab since 2020. He has published 85 international and 15 national research papers in reputed journals and 13 book chapters with an H-index of 33 and a total of 2832 citations on ResearchGate (https://www.researchgate.net/profile/Anurag-Malik-2), and 2692 citations on Google Scholar (https://scholar.google.com/citations?user=RdXgKsIAAAAJ&hl=en). He is also serving as a reviewer for more than 68 International journals. Dr. Malik is also a recipient of the Young Scientist Award for outstanding contribution to the field of Agricultural Engineering by the Agricultural Technology Development Society (ATDS) in 2021, Best Reviewer Award from Hydrological Sciences Journal in 2022, Best Popular Article Award from Ecofarming magazine in 2022, and placed in Top 2% World Scientists List in 2022. 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.
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.
Dr. Sonam Sandeep Dash is working as a Postdoctoral Researcher in the School of Civil Engineering at the University College Dublin. He received his Ph.D. in Water Resources Engineering from IIT Kharagpur in 2022 and have since developed a strong research agenda focused on physically-based hydrological model development, modelling hydrological extremes, and remote sensing derived vegetation mapping. Dr. Dash's research has been published in top-tier journals in Water Resources Engineering and has been presented at international conferences such as American Geophysical Union, European Geophysical Union. Their current research focuses on sustainable catchment management, particularly interested in integrated process-based models and remote sensing-based approaches for modelling hydrological extreme events. He has experience teaching courses in surface water hydrology, remote sensing and GIS application in water resources management, river basin management, and environmental hydrology and has received positive feedback from students for their engaging and innovative teaching methods. He has also mentored graduate and undergraduate students, helping them develop the skills and expertise necessary to succeed in water resources engineering and earth science applications. Beyond his research and teaching, Dr. Dash is committed to promoting diversity and inclusion in the relevant field. He is also an active member of AGU and EGU and has served in various academic roles, including judging student activities and outreach events.
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.
Dr. Sonam Sandeep Dash is working as a Postdoctoral Researcher in the School of Civil Engineering at the University College Dublin. He received his Ph.D. in Water Resources Engineering from IIT Kharagpur in 2022 and have since developed a strong research agenda focused on physically-based hydrological model development, modelling hydrological extremes, and remote sensing derived vegetation mapping. Dr. Dash's research has been published in top-tier journals in Water Resources Engineering and has been presented at international conferences such as American Geophysical Union, European Geophysical Union. Their current research focuses on sustainable catchment management, particularly interested in integrated process-based models and remote sensing-based approaches for modelling hydrological extreme events. He has experience teaching courses in surface water hydrology, remote sensing and GIS application in water resources management, river basin management, and environmental hydrology and has received positive feedback from students for their engaging and innovative teaching methods. He has also mentored graduate and undergraduate students, helping them develop the skills and expertise necessary to succeed in water resources engineering and earth science applications. Beyond his research and teaching, Dr. Dash is committed to promoting diversity and inclusion in the relevant field. He is also an active member of AGU and EGU and has served in various academic roles, including judging student activities and outreach events.
Editor
Scientist, Department of Soil and Water Engineering, Punjab Agricultural University, India
Scientist, Regional Research Station, Punjab Agricultural University, India
Assistant Professor, Arid Hydrology and Water Systems, Texas A&M AgriLife Research, El Paso Center; Department of Biological and Agricultural Engineering, Texas A&M University, USA
Assistant Professor, Shahid Chamran University of Ahvaz, Ahaz, Iran; Postdoctoral Research Associate, University of Prince Edward Island, Canada
Postdoctoral Researcher, School of Civil Engineering, University College Dublin, Ireland
Content
Section A: Applications of Geospatial Techniques for Soil Erosion Assessment
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.
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.