THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

developed in MATLAB using it’s ANN toolbox, eventually to automate the process
codes were developed for the development and assessment of the ANN.
Several networks were created and evaluated; to keep track of the networks the
convention used to label the network is ANN 5-7-20, where the first number represented
the texture, the second the number of input nodes and thus the number of input data
required and the third number represented the number of hidden nodes.
Initially only four parameters are considered; organic carbon, temperature, water filled
porosity and nitrate concentration. As there are only four parameters considered, a
network with four input nodes, two hidden nodes and one output node is developed for
each texture (X-4-2 network, where X represents the textural class).
The network is trained repeatedly till there is no improvement in perfromance. For each
new training run the weights and biases are reset to zero. If after a repeated number of
training runs (30) there is no improvement in the results the number of hidden nodes is
increased and the process restarted. This process is continued till there is no significant
improvement in the results. A maximum limit of twenty hidden neurons is set before the
entire set of networks developed for the texture is abandoned.
Besides the four selected parameters above, networks were developed by gradually
increasing the number of input variables and thus the number of input nodes. This
resulted in an improvement in the accuracy of some of the networks. During the
development of artificial networks for Sand, Sandy loam and Clay the best results are
obtained with 20 hidden neurons, this is true regardless of the number of inputs. In many
cases the results of the networks did not differ much between the use of 15-20 hidden
neurons. In order to keep a consistent fromat all networks are developed using 20 hidden
neurons. The necessity to use a higher number of hidden neurons is reflective of the
complexity of the process. The use of such a large number of hidden nodes necessitates
scrutiny of the network to prevent over-fitting, the use of the validation dataset is thus
essential to the process.
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