Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling, 1st Edition (Hardback) book cover


Data Integrative Approaches in Computation, Analysis, and Modeling, 1st Edition

By Praveen Kumar, Mike Folk, Momcilo Markus, Jay C. Alameda

CRC Press

552 pages | 258 B/W Illus.

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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.


"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

Table of Contents

Data Integrative Studies in Hydroinformatics; Praveen Kumar

. What is Hydroinformatics?

. Scope of the Book

. References


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


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


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


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


Statistical Data Mining; Amanda B.White and Praveen Kumar

. Supervised Learning

. Unsupervised Learning

. References

Neural Networks; Momcilo Markus

. Introduction

. Back-Propagation Neural Networks

. Synthetic Data Generation Based on Neural Networks

. Radial Basis Neural Networks: Minimal Resource Allocation Networks

. References

Genetic 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

. References

Fuzzy Logic; Lydia Vamvakeridou-Lyroudia and Dragan Savic

. Introduction

. Fuzzy Sets Essentials

. Fuzzy Modeling

. Fuzzy Reasoning Tutorial: An Example

. References


Appendix A: A Tutorial for Geodatabase and Modelshed Tools Operation

Appendix B

Appendix C: The UTM Northern Hemisphere Projection

Appendix D: Molodensky Equations

Appendix E: Section IV Review Questions

Appendix F: Section IV Project Assignment


About the Authors

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

Subject Categories

BISAC Subject Codes/Headings:
SCIENCE / Environmental Science
TECHNOLOGY & ENGINEERING / Environmental / General
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems