THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

Three statistical methods were used to estimate the denitrification rate, linear regressions
with Monte Carlo simulation, Multi Regression analysis and the development of a neural
network. Denitrification rates were found to be dependent on the WFP as well as organic
carbon. For the linear regressions a predictive relationship could not be established
between WFP and the denitrification rate. In addition, although an increase in organic
carbon content is typically assumed to increase denitrification, a linear relationship
between organic carbon and the denitrification rate could not be obtained unless the
additional controlling parameters are fixed. Stable isotope data is used to predict the
percent of nitrate removed due to denitrification. This method serves as an alternative to
estimate the loss of nitrate due to denitrification, but is unable to estimate a rate of
denitrification.
The developed methods are then applied to three study areas in Jacksonville and the
estimated denitrification rates from the methods are compared. Overall the results from
the each of the methods except for the multi-regression analysis are a reasonable estimate
of the denitrification rate. Due to the complexity of denitrification it is the Neural
Networks that are able to best estimate the denitrification rate. Thus by using easily
available parameters and existing data the models are able to match or improve the
accuracy in predicting the denitrification rate at a fraction of the cost without requiring
site specific data.
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