Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling

ISBN-13: 9780367453978 / Angielski / Miękka / 2019 / 552 str.

Praveen Kumar;Mike Folk;Momcilo Markus

Opis

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.

Recenzje

"The book makes a real contribution in bridging different disciplines, and the authors are to be congratulated for preparing this book on hydroinformatics…well-written, is easy to follow, and comprehensive. It is extremely timely and sends a clear message that teaching hydrology must entail hydroinformatics if hydrology is to take full advantage of emerging technologies, which are heavily based on new information and computing tools…will serve as a good textbook for a course on hydroinformatics either at the senior undergraduate level or the beginning graduate level…a useful reference book on one's bookshelf."

- Vijay P. Singh, Journal of Hydrologic Engineering, Vol. 11, No. 4

Spis treści

Data Integrative Studies in Hydroinformatics; Praveen Kumar. What is Hydroinformatics?. Scope of the Book. ReferencesDATA DRIVEN INVESTIGATION IN HYDROLOGYUnified Modeling Language; Benjamin L.Ruddell and Praveen Kumar. What is UML?. The Framework of the UML. Object Model Diagrams. Database Design and Deployment.. References. AbbreviationsDigital Library Technology; John J.Helly. Introduction. Building the Hydrologic Information System Digital Library. ReferencesHydrologic Metadata; Michael Piaseki. Introduction to Metadata.. Definition of Metadata Categories. Metadata: Problems and Standardization. Hydrologic Metadata. ReferencesHydrologic Data Models; Benjamin L.Ruddell and Praveen Kumar. Data Models. Geodata Models. The ArcHydro Data Model. References. AbbreviationsModelshed Data Model; Benjamin L.Ruddell and Praveen Kumar. Modelshed Framework. The Modelshed Geodata Model Structure. AbbreviationsMANAGING AND ACCESSING LARGE DATASETSData 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. ReferencesData Formats; Michael J.Folk. Formats and Abstraction Layers. Concepts of Data File Formats. Summary. ReferencesHDF5; 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. ReferencesDATA COMMUNICATIONWeb Services; Jay Alameda. Distributed Object Systems. Web Services. ReferencesXML; Jay Alameda. Data Descriptions. Task Descriptions in XML. ReferencesGrid Computing; Jay Alameda. Grid Genesis. Protocol-Based Grids. Service Grids. Application Scenarios. ReferencesIntegrated 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. ReferencesDATA PROCESSING AND ANALYSISIntroduction to Data Processing; Peter Bajcsy. Introduction to Section IV. Motivation Example. NSF Funded Applications. Overview of Section IV. Terminology. ReferencesUnderstanding 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. ReferencesData Representation; Peter Bajcsy. Introduction. Vector Data Types. Raster Data Types. Summary. ReferencesSpatial Registration; Peter Bajcsy. Introduction. Spatial Registration Steps. Computational Issues Related to Spatial Registration. Summary. ReferencesGeoreferencing; Peter Bajcsy. Introduction. Georeferencing Models. Geographic Transformations. Finding Georeferencing Information. Summary. ReferencesData Integration; Peter Bajcsy. Introduction. Spatial Interpolation with Kriging. Shallow Integration of Geospatial Raster Data. Deep Integration of Raster and Vector Data. Summary. ReferencesFeature Extraction; Peter Bajcsy. Introduction. Feature Extraction from Point Data.. Feature Extraction from Raster Data. Summary. ReferencesFeature 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. ReferencesSOFT COMPUTINGStatistical Data Mining; Amanda B.White and Praveen Kumar. Supervised Learning. Unsupervised Learning. ReferencesNeural Networks; Momcilo Markus. Introduction. Back-Propagation Neural Networks. Synthetic Data Generation Based on Neural Networks. Radial Basis Neural Networks: Minimal Resource Allocation Networks. ReferencesGenetic Algorithms; Barbara Minsker. Introduction. GA Basics. Formulating Hydroinformatics Optimization Problems: A Case Study in Groundwater Monitoring Design. GA Theory. Design Methodology for SGA Parameter Setting and Finding the Optimal Solution. Overcoming Computational Limitations. Advanced GAs. ReferencesFuzzy Logic; Lydia Vamvakeridou-Lyroudia and Dragan Savic. Introduction. Fuzzy Sets Essentials. Fuzzy Modeling. Fuzzy Reasoning Tutorial: An Example. ReferencesAPPENDICESAppendix A: A Tutorial for Geodatabase and Modelshed Tools OperationAppendix BAppendix C: The UTM Northern Hemisphere ProjectionAppendix D: Molodensky EquationsAppendix E: Section IV Review QuestionsAppendix F: Section IV Project AssignmentINDEX

O autorze

Kumar\, Praveen; Folk\, Mike; Markus\, Momcilo; Alameda\, Jay C.