THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

ational predictions in contaminated areas. While acknowledging that the perfromance of
the MNN was less than the classical fate and transport model, they found that the
accuracy is good enough for it to be considered as a preliminary tool of analysis.
In a recent study Zuo (2008) has shown that it is possible to estimate the amount of
nitrogen removal due to denitrification using an artificial neural network. The artificial
network was created using MATLAB Neural Network Tool box 4, and had nine input
nodes and three output nodes. After training the network, accuracies for prediction of
nitrate concentrations was + 10 % thus demonstrating that accurate predictions are
possible.
In a study of denitrification in a constructed wetland in Seoul, South Korea, Song et al.
(2006) were successful in using a multi-layer perceptron network to predict
denitrification. Their results show 91% accuracy in the prediction of denitrification
suggesting that ANNs can be a powerful tool in estimating the rate of denitrification.
While the above works suggest the ANNs are useful in the estimation of the
denitrification rate, all of the studies are based on a very small local database. The
selection of the data in this manner in essence assures the success of using an ANN to
predict denitrification.
The first attempt to apply ANNs in predicting the denitrification rate across wider region
is by Oehler et al., (2010). They designed an ANN to simulate N emissions from the
denitrification process at the field scale. The perfromance of the ANN outside the training
(calibration) dataset space is not assessed and it can display physically unrealistic
behavior (Oehler et al., 2010). To improve prediction accuracy and increase model
generality Oehler et al., 2010 suggest that the database will need to be larger to account
for various types of soil (with more clay notably). The database used in this work
contains 1129 records as opposed to the 449 records used by Oehler et al. (2010).
5.3. Building the neural network and code
Based on the work done by Oehler et al., (2010), Zuo (2008) and Song et al (2006), a two
layer feed-forward network with sigmoid hidden layers and linear output neurons was
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