Hydroinformatics (inbunden)
Inbunden (Hardback)
Antal sidor
illustrated ed
CRC Press Inc
Markus, Momcilo
177 equations; 24 Tables, black and white; 258 Illustrations, black and white
254 x 177 x 38 mm
1133 g
Antal komponenter
Hydroinformatics (inbunden)


Data Integrative Approaches in Computation, Analysis, and Modeling

Inbunden Engelska, 2005-11-01
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Modern 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, approaches, and system considerations necessary to take full advantage of the abundant hydrological data available today. Linking hydrological science with computer engineering, networking, and database science, this book lays a pedagogical foundation in the concepts underlying developments in hydroinformatics. It begins with an introduction to data representation through Unified Modeling Language (UML), followed by digital libraries, metadata, the basics of data models, and Modelshed, a new hydrological data model. Building on this platform, the book discusses integrating and managing diverse data in large datasets, data communication issues such as XML and Grid computing, the basic principles of data processing and analysis including feature extraction and spatial registration, and modern methods of soft computing such as neural networks and genetic algorithms. Today, hydrological data are increasingly rich, complex, and multidimensional. Providing a thorough compendium of techniques and methodologies, Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling is the first reference to supply the tools necessary to confront these challenges successfully.
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Bloggat om Hydroinformatics


Data Integrative Studies in Hydroinformatics; Praveen Kumar . What is Hydroinformatics? . Scope of the Book . References DATA DRIVEN INVESTIGATION IN HYDROLOGY Unified Modeling Language; Benjamin L.Ruddell and Praveen Kumar . What is UML? . The Framework of the UML . Object Model Diagrams . Database Design and Deployment. . References . Abbreviations Digital Library Technology; John J.Helly . Introduction . Building the Hydrologic Information System Digital Library . References Hydrologic Metadata; Michael Piaseki . Introduction to Metadata. . Definition of Metadata Categories . Metadata: Problems and Standardization . Hydrologic Metadata . References Hydrologic Data Models; Benjamin L.Ruddell and Praveen Kumar . Data Models . Geodata Models . The ArcHydro Data Model . References . Abbreviations Modelshed Data Model; Benjamin L.Ruddell and Praveen Kumar . Modelshed Framework . The Modelshed Geodata Model Structure . Abbreviations MANAGING AND ACCESSING LARGE DATASETS Data Models for Storage and Retrieval; Michael J.Folk . Survey of Different Types and Uses of Data . Who are the Users? . Gathering, Using, and Archiving Data . Data Management Challenges . Summary . References Data Formats; Michael J.Folk . Formats and Abstraction Layers . Concepts of Data File Formats . Summary . References HDF5; Michael J.Folk . What is HDF5? . HDF5 Data Model: Drilling Down . HDF5 Library . Example Problem: Using the HDF5 File Format as IO for an Advection -Diffusion Model . References DATA COMMUNICATION Web Services; Jay Alameda . Distributed Object Systems . Web Services . References XML; Jay Alameda . Data Descriptions . Task Descriptions in XML . References Grid Computing; Jay Alameda . Grid Genesis . Protocol-Based Grids . Service Grids . Application Scenarios . References Integrated Data Management; Seongeun Jeong,Yao Liang,and Xu Liang . Introduction . Metadata and Integrated Data Management . Metadata Mechanism for Data Management . Data Management System Using Metadata Mechanism . Development of an Integrated Data Management System . Conclusions . References DATA PROCESSING AND ANALYSIS Introduction to Data Processing; Peter Bajcsy . Introduction to Section IV . Motivation Example . NSF Funded Applications . Overview of Section IV . Terminology . References Understanding Data Sources; Peter Bajcsy . Introduction . Data Sources from Data Producers . Example of Data Generation for Modeling BRDFs . Example of Data Acquisitions Using Wireless Sensor Networks . Summary . References Data Representation; Peter Bajcsy . Introduction . Vector Data Types . Raster Data Types . Summary . References Spatial Registration; Peter Bajcsy . Introduction . Spatial Registration Steps . Computational Issues Related to Spatial Registration . Summary . References Georeferencing; Peter Bajcsy . Introduction . Georeferencing Models . Geographic Transformations . Finding Georeferencing Information . Summary . References Data Integration; Peter Bajcsy . Introduction . Spatial Interpolation with Kriging . Shallow Integration of Geospatial Raster Data . Deep Integration of Raster and Vector Data . Summary . References Feature Extraction; Peter Bajcsy . Introduction . Feature Extraction from Point Data. . Feature Extraction from Raster Data . Summary . References Feature Selection and Analysis; Peter Bajcsy . Introduction . General Feature Selection Problem . Spectral Band Selection Problem . Overview of Band Selection Methods . Conducting Band Selection Studies . Feature Analysis and Decision Support Example . Evaluation of Geographic Territorial Partitions and Decision Support . Summary . References SOFT COMPUTING Statistical Data Mining; Amanda B.White and Praveen Kumar . Supervised Learning . Unsupervised Learning . References Neural Networks; Mo