Improving artificial neural network model predictions of daily average PM10 concentrations by applying principle...

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.

This study provides an alternative approach to predict PM10 levels 1 day ahead by building seasonal ANN models. Applying PCA on a lagged data set resulted in selection of the most significant lags of variables reducing model complexity. Cascading training with error back-propagation method appropriately determined hidden layer neurons. Separately building ANN models for winter and summer periods over years, even though it required much more effort compared with building regular nonseasonal models, yielded better model agreements and smaller testing errors. This approach can be applied on the data sets with irregularities and a large number of considered inputs.