Land and Water Resources Management Using Machine Learning and Geospatial Techniques addresses critical knowledge gaps in hydrology, remote sensing, and soil and water conservation. This book will explore various methodologies for estimating soil loss, encompassing modeling techniques, geospatial methodologies, and machine learning approaches. This book will empower researchers in their pursuit of sustainable solutions for effective land and water management. Furthermore, it will explore the fusion of geospatial tools with ML-based models, fostering an innovative approach to resource management.…mehr
Land and Water Resources Management Using Machine Learning and Geospatial Techniques addresses critical knowledge gaps in hydrology, remote sensing, and soil and water conservation. This book will explore various methodologies for estimating soil loss, encompassing modeling techniques, geospatial methodologies, and machine learning approaches. This book will empower researchers in their pursuit of sustainable solutions for effective land and water management. Furthermore, it will explore the fusion of geospatial tools with ML-based models, fostering an innovative approach to resource management.
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.
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.
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