Handbook of HydroInformatics Volume III: Water Data Management Best Practices presents the latest and most updated data processing techniques that are fundamental to Water Science and Engineering disciplines. These include a wide range of the new methods that are used in hydro-modeling such as Atmospheric Teleconnection Pattern, CONUS-Scale Hydrologic Modeling, Copula Function, Decision Support System, Downscaling Methods, Dynamic System Modeling, Economic Impacts and Models, Geostatistics and Geospatial Frameworks, Hydrologic Similarity Indices, Hydropower/Renewable Energy Models, Sediment Transport Dynamics Advanced Models, Social Data Mining, and Wavelet Transforms.
This volume is an example of true interdisciplinary work. The audience includes postgraduates and above interested in Water Science, Geotechnical Engineering, Soil Science, Civil Engineering, Chemical Engineering, Computer Engineering, Engineering, Applied Science, Earth and Geoscience, Atmospheric Science, Geography, Environment Science, Natural Resources, Mathematical Science, and Social Sciences. It is a fully comprehensive handbook which provides all the information needed related to the best practices for managing water data.
1. Handbook of HydroInformatics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. Contributors - p. Page v 8. About the Editors - p. Page vi 9. Preface - p. Page viii 10. List of Illustrations - p. Page ix 11. List of Tables - p. Page x 12. Chapter 1 : Advantage of grid-free analytic element method for identification of locations and pumping rates of wells - p. Page x 13. Chapter 2 : Application of experimental data and soft computing techniques in determining the outflow and breach characteristics in embankments and landslide dams - p. Page 7 14. Chapter 3 : Hydrological modeling of Hasdeo River Basin using HEC-HMS - p. Page 29 15. Chapter 4 : Application of soft computing methods in turbulent storm water modeling - p. Page 49 16. Chapter 5 : Assessment of bed load transport for steep channels on the basis of conventional and fuzzy regression - p. Page 64 17. Chapter 6 : Automated flood inundation mapping over Ganga basin - p. Page 78 18. Chapter 7 : Causal reasoning modeling (CRM) for rivers’ runoff behavior analysis and prediction - p. Page 85 19. Chapter 8 : Data assimilation in hydrological and hazardous forecasting - p. Page 106 20. Chapter 9 : Flood routing computations - p. Page 119 21. Chapter 10 : Application of fuzzy logic in water resources engineering - p. Page 152 22. Chapter 11 : GIS Application in floods mapping in the Ganges–Padma River basins in Bangladesh - p. Page 165 23. Chapter 12 : Groundwater level forecasting using hybrid soft computing techniques - p. Page 180 24. Chapter 13 : Hydroinformatics methods for groundwater simulation - p. Page 204 25. Chapter 14 : Hydrological-Hydraulic Modeling of floodplain inundation: A case study in Bou Saâda Wadi—Subbasin_Algeria - p. Page 215 26. Chapter 15 : Interoceanic waterway network system - p. Page 223 27. Chapter 16 : Lattice Boltzmann models for hydraulic engineering problems - p. Page 243 28. Chapter 17 : Developments in sediment transport modeling in alluvial channels - p. Page 254 29. Chapter 18 : Modeling approaches for simulating the processes of wetland ecosystems - p. Page 262 30. Chapter 19 : Multivariate linear modeling for the application in the field of hydrological engineering - p. Page 273 31. Chapter 20 : Ontology-based knowledge management framework: Toward CBR-supported risk response to hydrological cascading disasters - p. Page 287 32. Chapter 21 : Optimally pruned extreme learning machine: A new nontuned machine learning model for predicting chlorophyll concentration - p. Page 297 33. Chapter 22 : Proposing model for water quality analysis based on hyperspectral remote sensor data - p. Page 315 34. Chapter 23 : Real-time flood hydrograph predictions using rating curve and soft computing methods (GA, ANN) - p. Page 322 35. Chapter 24 : River Bathymetry acquisition techniques and its utility for river hydrodynamic modeling - p. Page 338 36. Chapter 25 : Runoff modeling using group method of data handling and gene expression programming - p. Page 349 37. Chapter 26 : Sediment transport with soft computing application for tropical rivers - p. Page 370 38. Index - p. Page 392
Handbook of HydroInformatics Volume III: Water Data Management Best Practices presents the latest and most updated data processing techniques that are fundamental to Water Science and Engineering disciplines. These include a wide range of the new methods that are used in hydro-modeling such as Atmospheric Teleconnection Pattern, CONUS-Scale Hydrologic Modeling, Copula Function, Decision Support System, Downscaling Methods, Dynamic System Modeling, Economic Impacts and Models, Geostatistics and Geospatial Frameworks, Hydrologic Similarity Indices, Hydropower/Renewable Energy Models, Sediment Transport Dynamics Advanced Models, Social Data Mining, and Wavelet Transforms.
This volume is an example of true interdisciplinary work. The audience includes postgraduates and above interested in Water Science, Geotechnical Engineering, Soil Science, Civil Engineering, Chemical Engineering, Computer Engineering, Engineering, Applied Science, Earth and Geoscience, Atmospheric Science, Geography, Environment Science, Natural Resources, Mathematical Science, and Social Sciences. It is a fully comprehensive handbook which provides all the information needed related to the best practices for managing water data.
1. Handbook of HydroInformatics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Dedication - p. Page v 7. Contributors - p. Page v 8. About the Editors - p. Page vi 9. Preface - p. Page viii 10. List of Illustrations - p. Page ix 11. List of Tables - p. Page x 12. Chapter 1 : Advantage of grid-free analytic element method for identification of locations and pumping rates of wells - p. Page x 13. Chapter 2 : Application of experimental data and soft computing techniques in determining the outflow and breach characteristics in embankments and landslide dams - p. Page 7 14. Chapter 3 : Hydrological modeling of Hasdeo River Basin using HEC-HMS - p. Page 29 15. Chapter 4 : Application of soft computing methods in turbulent storm water modeling - p. Page 49 16. Chapter 5 : Assessment of bed load transport for steep channels on the basis of conventional and fuzzy regression - p. Page 64 17. Chapter 6 : Automated flood inundation mapping over Ganga basin - p. Page 78 18. Chapter 7 : Causal reasoning modeling (CRM) for rivers’ runoff behavior analysis and prediction - p. Page 85 19. Chapter 8 : Data assimilation in hydrological and hazardous forecasting - p. Page 106 20. Chapter 9 : Flood routing computations - p. Page 119 21. Chapter 10 : Application of fuzzy logic in water resources engineering - p. Page 152 22. Chapter 11 : GIS Application in floods mapping in the Ganges–Padma River basins in Bangladesh - p. Page 165 23. Chapter 12 : Groundwater level forecasting using hybrid soft computing techniques - p. Page 180 24. Chapter 13 : Hydroinformatics methods for groundwater simulation - p. Page 204 25. Chapter 14 : Hydrological-Hydraulic Modeling of floodplain inundation: A case study in Bou Saâda Wadi—Subbasin_Algeria - p. Page 215 26. Chapter 15 : Interoceanic waterway network system - p. Page 223 27. Chapter 16 : Lattice Boltzmann models for hydraulic engineering problems - p. Page 243 28. Chapter 17 : Developments in sediment transport modeling in alluvial channels - p. Page 254 29. Chapter 18 : Modeling approaches for simulating the processes of wetland ecosystems - p. Page 262 30. Chapter 19 : Multivariate linear modeling for the application in the field of hydrological engineering - p. Page 273 31. Chapter 20 : Ontology-based knowledge management framework: Toward CBR-supported risk response to hydrological cascading disasters - p. Page 287 32. Chapter 21 : Optimally pruned extreme learning machine: A new nontuned machine learning model for predicting chlorophyll concentration - p. Page 297 33. Chapter 22 : Proposing model for water quality analysis based on hyperspectral remote sensor data - p. Page 315 34. Chapter 23 : Real-time flood hydrograph predictions using rating curve and soft computing methods (GA, ANN) - p. Page 322 35. Chapter 24 : River Bathymetry acquisition techniques and its utility for river hydrodynamic modeling - p. Page 338 36. Chapter 25 : Runoff modeling using group method of data handling and gene expression programming - p. Page 349 37. Chapter 26 : Sediment transport with soft computing application for tropical rivers - p. Page 370 38. Index - p. Page 392
