Modeling Nitrate Loading Rate in Delaware Lakes Using Regression and Neural Networks
Date
2003-01
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Food and Resources Economics
Abstract
The 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.
Description
Keywords
Nitrogen, Delaware lakes, Neural network model