Modeling Nitrate Loading Rate in Delaware Lakes Using Regression and Neural Networks

Author(s)Sudhakar, Prachi
Author(s)Krishnan, Palaniappa
Author(s)Bernard, John C.
Author(s)Ritter, William F.
Date Accessioned2004-10-07T19:54:02Z
Date Available2004-10-07T19:54:02Z
Publication Date2003-01
AbstractThe objective of this research was to predict the nitrogen-loading rate to Delaware lakes and streams using regression analysis and neural networks. Both models relate nitrogen-loading rate to cropland, soil type and presence of broiler production. Dummy variables were used to represent soil type and the presence of broiler production at a watershed. Data collected by Ritter & Harris (1984) was used in this research. To build the regression model Statistical Analysis System (SAS) was used. NeuroShell Easy Predictor, neural network software was used to develop the neural network model. Model adequacy was established by statistical techniques. A comparison of the regression and neural network models showed that both perform equally well. Cropland was the only significant variable that had any influence on the nitrogen-loading rate according to both the models.en
Extent265557 bytes
MIME typeapplication/pdf
URLhttp://udspace.udel.edu/handle/19716/115
Languageen_US
PublisherDepartment of Food and Resources Economicsen
Part of SeriesSP03-02
KeywordsNitrogenen
KeywordsDelaware lakesen
KeywordsNeural network modelen
TitleModeling Nitrate Loading Rate in Delaware Lakes Using Regression and Neural Networksen
TypeStaff Paperen
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
SP03-02.pdf
Size:
259.33 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.31 KB
Format:
Item-specific license agreed upon to submission
Description: