Improving artificial neural network model and implementing seasonal models

What is it about?

This study introduces a seasonal modeling approach in the prediction of daily average PM10 (particulate matter with an aerodynamic diameter<10 μm) levels 1 day ahead based on multilayer perceptron artificial neural network (MLP-ANN) forecasters. The data set covered all daily based meteorological parameters and PM10 concentrations in the period of 2007-2014. Seasonal ANN models for winter and summer periods were separately developed and trained by using a lagged time series data set. The most significant lagged terms of the variables within a 1-week period were determined by principal component analysis (PCA) and assigned as input vectors of ANN models. Cascading training with error back-propagation method was applied in model building. The use of seasonal ANN models with PCA-based inputs showed an increased prediction performance compared with nonseasonal models. For seasonal ANN models, the overall model agreement in training between modeled and observed values varied in the range of 0.78-0.83 and R2 values ranged in 0.681-0.727, which outperformed nonseasonal models. The best testing R2 values of seasonal models for winter and summer periods ranged in 0.709-0.727 with lower testing error, and the models did not show a tendency towards overpredicting or underpredicting the PM10 levels. The approach demonstrated in the study appeared to be promising for predicting short-term levels of pollutants through the data sets with high irregularities and could have significant applicability in the case of large number of considered inputs.

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Why is it important?

This study attempts at designing individual seasonal ANN models for reasonable predictions of daily average PM10 concentrations 1 day ahead by using time series data, taking into account the effects from local meteorological conditions. A lagged time series data set was produced by back-shifting up to 7 days to select significant lags of variables as input vectors to the ANN models. Furthermore, PCA method was employed before constructing ANN models in determining of proper consecutive inputs from lagged variables in the data set.

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