Prediction Of TSP Concentration In A
Metallurgical City Of Brazil Using
Neural Networks
Author(s)
M. M. C. Lima
Abstract
The aim of this study was to predict Total Suspended Particulate concentration
(TSP) in the main areas of Ipatinga, a metallurgical city located in Minas Gerais
state, southeast of Brazil. Artificial neural networks (ANN) were the modelling
tool used. This model is able to predict pollutant concentration just by training
the input and output parameters. The input parameters were meteorological such
as wind direction, wind speed, rain, and ambient temperature and also seasonal
such as, summer and winter. The output parameter used was the historical data of
the total suspended particulate concentration taken between 1996 and 2004. In
the modelling, the multilayer perceptron (MLP) model was tested. Among the
MLP configurations evaluated, the topology 13-7-6 was chosen. The validation
of the model was done by comparing the simulated with the observed values.
The results of this model were also compared with the industrial source complex
short-term dispersion model (ISCST3). The four statistical tools used to evaluate
the fitting were mean squared error (MSE), fractional bias (FB), index of
agreement (IA) and linear correlation coefficient (R). Comparing the results it
was seen that the predicted values were better in some boroughs and were
overestimated in others. Besides, the predicted results of the ANN model were
better than the ISCST3 dispersion model.
Keywords: artificial neural networks modelling, multilayer perceptron, total
suspended particulate concentration, prediction, ISCST3 dispersion.
Keywords
artificial neural networks modelling, multilayer perceptron, total
suspended particulate concentration, prediction, ISCST3 dispersion
Related Book
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