Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and ModelingModern hydrology is more interdisciplinary than ever. Staggering amounts and varieties of information pour in from GIS and remote sensing systems every day, and this information must be collected, interpreted, and shared efficiently. Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling introduces the tools, approache |
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... coordinate systems (spatial registration, georeferencing) and temporal synchronization; (4) data integration (integration and fusion of related or unrelated features over common spatial and temporal regions); (5) feature extraction; (6) ...
... coordinate systems (spatial registration, georeferencing) and temporal synchronization; (4) data integration (integration and fusion of related or unrelated features over common spatial and temporal regions); (5) feature extraction; (6) ...
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... coordinate the metadata needs (and sometimes habits) among many individuals within a community like the Hydrologic Sciences, or even across communities. These thoughts will accompany us in the following sections where we will try to ...
... coordinate the metadata needs (and sometimes habits) among many individuals within a community like the Hydrologic Sciences, or even across communities. These thoughts will accompany us in the following sections where we will try to ...
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Contents
7 | |
Managing and Accessing Large Datasets | 101 |
Data Communication | 161 |
Data Processing and Analysis | 257 |
Soft Computing | 379 |
Appendices | 477 |
Index | 529 |
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algorithm analysis application approach associated attributes bands boundary called Center chapter clusters complex component contains coordinate create data model data structures database datasets defined described distance distributed elements Engineering Equation error example extraction feature field Figure format function fuzzy geographic given grid HDF5 hydrologic illustrated implementation important input integration interface logical mean measurements metadata methods Modelshed multiple Name node objects operations optimization organized original output parameters perform prediction presented problem projection properties query raster records References relationship represent requires Research rules schema segment selection sensor shown simple sources spatial specific standard step storage stored structure transformation tree University values variables vector visualization xs:element