GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA
Publication: Journal of Environmental Engineering
Volume 141, Issue 5
Abstract
The prediction of stream water quality (WQ) is essential to understand and quantitatively describe water quality parameters (which include physical characteristics, inorganic metallic, and nonmetallic concentrations) and their structure, watershed health, biodiversity, and ecology of a basin. The spatial variability and temporal randomness of stream water quality parameters makes the problem a complex modeling task by ordinary statistical regression methods. The determination of water quality parameters and their spatial and temporal description in stream networks is even more complex due to the stochastic nature of water flow, atmospheric conditions, meteorological patterns, and nonlocal effects of precipitation and temperature. In this paper, a statistical, geographic information system (GIS) and a neural network based water quality model is developed to study stream water quality parameter structure in a geographic framework in the United States of America (USA) consisting of stream network, watershed, and a variety of different land-use practices. Also, a novel way of representing land use in the form of land-use factor (LUF) is formulated for modeling purposes.
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Acknowledgments
The authors gratefully acknowledge funding by the U.S. Environmental Protection Agency, award number X-97418901-0 to Western Kentucky University (WKU), in support of this work. We also acknowledge the support of the WKU Green River Preserve and the cooperation of Upper Green River Watershed Watch and statewide partners. We would like to express our gratitude to Tim Rink (GIS Analyst, WKU), Dr. Claire Rinehart (WKU), Jenna Harbaugh (GIS Analyst, WKU), and Dr. Stuart Foster (Kentucky Climate Center, WKU) for help with data. We thank Professor Vinay Prasad, Department of Chemical and Materials Engineering, University of Alberta, Canada, for helpful suggestions.
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Published In
Journal of Environmental Engineering
Volume 141 • Issue 5 • May 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 18, 2012
Accepted: Oct 21, 2013
Published online: Nov 4, 2014
Discussion open until: Apr 4, 2015
Published in print: May 1, 2015
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