A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)
The forecasting of air pollution is important for living environment and public health. The prediction of SO2 (sulfur dioxide), which is one of the indicators of air pollution, is a significant part of steps to be done in order to decrease the air pollution. In this study, a novel feature scaling method called neighbor-based feature scaling (NBFS) has been proposed and combined with artificial neural network (ANN) and adaptive networkbased fuzzy inference system (ANFIS) prediction algorithms in order to predict the SO2 concentration value is from air quality metrics belonging to Konya province in Turkey. This work consists of two stages. In the first stage, SO2 concentration dataset has been scaled using neighbor-based feature scaling. In the second stage, ANN and ANFIS prediction algorithms have been used to forecast the SO2 value of scaled SO2 concentration dataset. SO2 concentration dataset was obtained from Air Quality Statistics database of Turkish Statistical Institute. To constitute dataset, the mean values belonging to seasons of winter period have been used with the aim of watching the air pollution changes between dates of December, 1, 2003 and December, 30, 2005. In order to evaluate the performance of the proposed method, the performance measures including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and IA (Index of Agreement) values have been used. After NBFS method applied to SO2 concentration dataset, the obtained RMSE and IA values are 83.870.27 (IA) and 930.33 (IA) using ANN and ANFIS, respectively. Without NBFS, the obtained RMSE and IA values are 85.310.25 (IA) and 117.710.29 (IA) using ANN and ANFIS, respectively. The obtained results have demonstrated that the proposed feature scaling method has been obtained very promising results in the prediction of SO2 concentration values.
air pollution neighbor-based feature scaling feature scaling so2 prediction