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Eventually the final set of networks evaluated contained 7 input Nodes, 20 hidden nodes
and one output node. This corresponded to the 7 input parameters that were compiled for
each denitrification value. Each network is evaluated in terms of the error (Error =
Predicted– Observed) or percentage error described as
146
( edicted − Observed )
∗100
Error =
Observed
Pr
% .
While the database used in this work is more than double the amount used on Oehler et
al. (2010) it is still insufficient to expand the method to encompass all possible textural
and soil profile scenarios. Due to the paucity of the data no attempt is made to develop
neural networks for the following textures; Silt (no data), Sandy clay (no data), Loamy
sand (n=4) and Sandy clay loam (n=5). While neural networks are developed for clay
(n=27), it is done with the understanding that the results bound to be unreliable.
Realistically, the only networks that may be considered reliable are Clay Loam (n=77),
Loam (n=102), Sand (n=103), Sandy Loam (n=181), Silty loam (n=260), Silty clay
(n=283) and Silty clay loam (n=71).
5.4. Results
5.4.1. Texture 1 (Clay)
With only 27 records the dependability of the network developed for the clay subset is
unreliable. One record is deleted due to a missing value and the network is developed on
the remaining 26 records. The network used is ANN 1-7-20. As can be seen in Figure 5.4,
when compared to the earlier methods, the network perfroms quiet well for the training
and validation but does not perfrom as well for the test dataset. This is because the ANN
does not have sufficient data to learn. The error ranges from 1117 % - 0.62 %. There are
five denitrification rates with high error percentages (> 100 %), these are mainly from the
test dataset, the percent error for the training and validation dataset is between 87.08 % -
0.62 %.