Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling, 1st Edition (e-Book) 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

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pub: 2005-11-02
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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, approache

Table of Contents

Data Integrative Studies in Hydroinformatics. DATA DRIVEN INVESTIGATION IN HYDROLOGY. Unified Modeling Language. Digital Library Technology. Hydrologic Metadata. Hydrologic Data Models. Modelshed Data Model. MANAGING AND ACCESSING LARGE DATASETS. Data Models for Storage and Retrieval. Data Formats. HDF5. DATA COMMUNICATION. Web Services. XML. Grid Computing. Integrated Data Management. DATA PROCESSING AND ANALYSIS. Introduction to Data Processing. Understanding Data Sources. Data Representation. Spatial Registration. Georeferencing. Data Integration. Feature Extraction. Feature Selection and Analysis. SOFT COMPUTING. Statistical Data Mining. Neural Networks. Genetic Algorithms. Fuzzy Logic. APPENDICES. INDEX.

About the Authors

Praveen Kumar, Ph.D. has been a faculty member in the Department of Civil and Environmental Engineering at the University of Illinois since 1995. Prior to joining the University of Illinois, he was a research scientist at the Universities Space Research Association (USRA) and Hydrologic Sciences Branch, NASA — Goddard Space Flight Center, Greenbelt, Massachusetts. His expertise is in large scale hydrologic processes with an emphasis on hydroclimatology, hydroecology, estimation and data assimilation, geomorphology, and hydro informatics. He obtained his Bachelor of Technology from the Indian Institute of Technology, Bombay (1987), Master of Science from the Iowa State University (1989), and Ph.D. from the University of Minnesota (1993), all in civil engineering.

Mike Folk, Ph.D. has been with the Hierarchical Data Format (HDF) Group at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign since 1988, leading the HDF group during that period. Dr. Folk’s professional interests are primarily in the area of scientific data management. Through his work with HDF, Dr. Folk is heavily involved with data management issues in NASA andthe earth science community. He has also helped to lead the effort to provide a standard format to address data management needs for the Department of Energy’s ASCI project, which involves data I/O, storage, and sharing among tera-scale computing platforms. Before joining NCSA, Dr. Folk taught computer science at the university level for 18 years. Among Dr. Folk’s publications is the book File Structures, A Conceptual Toolkit. Dr. Folk earned his Ph.D. in computer science from Syracuse University in 1974, an M.A.T. in mathematics teaching from the University of Chicago in 1966, andB.S. in mathematics from the University of North Carolina, Chapel Hill, in 1964.

Momcilo Markus, Ph.D. has extensive research, industry, and teaching experience dealing with water issues in Europe and in the United States. He is currently a research hydrologist at the Illinois State Water Survey evaluating hydrologic changes in watersheds, nutrient load estimation methods, and nutrient-based watershed classification. Formerly Dr. Markus worked with the National Weather Service’s river forecasting system, FEMA’s flood insurance mapping program, and various other activities. His specialties include statistical/stochastic hydrology, hydrologic modeling, water resources management, data mining, pattern recognition, and water quality statistics. He earned his B.S. and M.S. at the University of Belgrade, and Ph.D. from the Colorado State University.

Jay C.Alameda has been with the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign since 1990. Currently, Alameda is the lead for the NCSA effort in middleware, which encompasses grid computing environments for applications as well as gridd evelopment. In this role, he has worked to develop cyberinfrastructure in service of advanced environments for atmospheric discovery and advanced multiscale chemical engineering, in the form of configurable, reuseable workflow engines andclient-sid e tools andsupporting services. In developing these advanced environments, Alameda has worked with partners in the Alliance Expeditions (MEAD and Science Portals), and is now working with the Linked Environments for Atmospheric Discovery (LEAD) and Open Grid Computing Environments Consortium (OGCE) efforts. Through the course of developing application grid computing infrastructure, Alameda also works to ensure that NCSA and Tera Grid production resources are capable of supporting these new application use modalities. Alameda continues to cultivate collaborations with new discipline areas, such as the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), in order to help develop advanced capabilities to meet their scientific needs, and better inform the development of cyberinfrastructure, and is working to connect developments within NCSA through service architectures. He earned his Master of Science in nuclear engineering from the University of Illinois at Urbana-Champaign (1991), and Bachelor of Science in chemical engineering from the University of Notre Dame (1986).

Peter Bajcsy, Ph.D. has been with the Automated Learning Group at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign (UIUC), since 2001 working as a research scientist on problems related to automatic transfer of image content to knowledge. He has been an adjunct assistant professor of the Department of Electrical and Computer Engineering and the Department of Computer Science at UIUC since 2002 teaching and advising graduate students. Before joining NCSA, he had worked on real-time machine vision problems for the semiconductor industry and synthetic aperture radar (SAR) technology for the government contracting industry. He has developed several software systems for automatic feature extraction, feature selection, segmentation, classification, tracking, and statistical modeling from electro-optical, SAR, laser and hyperspectral datasets. Dr. Bajcsy’s scientific interests include image and signal processing, statistical data analysis, data mining, pattern recognition, novel sensor technology, and computer and machine vision. He earned his Ph.D. from the Electrical and Computer Engineering Department, University of Illinois at Urbana-Champaign, 1997, M.S. degree from the Electrical Engineering Department, University of Pennsylvania, Philadelphia, 1994, and Diploma Engineer degree from the Electrical Engineering Department, Slovak Technical University, Bratislava, Slovakia, 1987.

Subject Categories

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