Land and Water Resources Management Using Machine Learning and Geospatial Techniques: Developments in Environmental Science
Editat de Mahesh Chand Singh, Anurag Malik, Santosh Subhash Palmate, Mehdi Jamei, Sonam Sandeep Dashen Limba Engleză Paperback – iun 2026
- 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
- Explore the fusion of geospatial tools with ML-based models
Preț: 673.80 lei
Preț vechi: 740.45 lei
-9% Precomandă
Puncte Express: 1011
Preț estimativ în valută:
119.25€ • 139.85$ • 104.56£
119.25€ • 139.85$ • 104.56£
Carte nepublicată încă
Doresc să fiu notificat când acest titlu va fi disponibil:
Se trimite...
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780443363061
ISBN-10: 0443363064
Pagini: 400
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Developments in Environmental Science
ISBN-10: 0443363064
Pagini: 400
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Developments in Environmental Science
Cuprins
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.