Document Type

Thesis

Date of Degree

Summer 2011

Degree Name

MS (Master of Science)

Degree In

Industrial Engineering

First Advisor

Andrew Kusiak

Abstract

Water is vital to man and its quality it a serious topic of concern. Addressing sustainability issues requires new understanding of water quality and water transport. Past research in hydrology has focused primarily on physics-based models to explain hydrological transport and water quality processes. The widespread use of in situ hydrological instrumentation has provided researchers a wealth of data to use for analysis and therefore use of data mining for data-driven modeling is warranted. In fact, this relatively new field of hydroinformatics makes use of the vast data collection and communication networks that are prevalent in the field of hydrology. In this Thesis, a data-driven approach for analyzing water quality is introduced. Improvements in the data collection of information system allow collection of large volumes of data. Although improvements in data collection systems have given researchers sufficient information about various systems, they must be used in conjunction with novel data-mining algorithms to build models and recognize patterns in large data sets. Since the mid 1990's, data mining has been successful used for model extraction and describing various phenomena of interest.

Keywords

Data Mining, Evolutionary Computation, Heuristic search methods, Hydrology, Neural Network, Water Quality

Pages

xi, 71 pages

Bibliography

Includes bibliographical references (pages 67-71).

Copyright

Copyright 2011 Evan Roz

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