Abstract
The forecast of the CO and NO2 concentration levels has been obtained by the 3-layer Perceptron Neural Network with Error Backpropagation learning rule.
This study shows the 3-layer Perceptron performances in relationship with the choice of the activation functions parameters (depending on the statistical values of the input/output variables).
A first simulation set using data at 1 hour before has been utilized to forecast CO levels. A second simulation set with data at 12–24 hours before has been used to forecast NO2 levels.
The Neural Net's performance appears to be very good both for the parameters activation function optimisation and the variables choice.
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Pelliccioni, A., Poli, U. Use of Neural Net Models to Forecast Atmospheric Pollution. Environ Monit Assess 65, 297–304 (2000). https://doi.org/10.1023/A:1006419504230
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DOI: https://doi.org/10.1023/A:1006419504230