Usage of output-dependent data scaling in modeling and prediction of air pollution daily concentration values (PM₁₀) in the city of Konya

This paper presents the combination of a data preprocessing called output-dependent data scaling (ODDS) and adaptive network-based fuzzy inference system (ANFIS) to predict the air pollution daily levels including particulate matter (PM₁₀) concentration values belonging to the city of Konya in Turkey. Also, we have used the regression models including least square regression, partial least square regression, and multivariate linear regression as prediction models in addition to ANFIS model. Data transformation or normalization methods should be used to increase the performance of used prediction models and are used prior to prediction algorithms. In this study, we have used the output-dependent data scaling method as data transformation method and combined it with ANFIS and regression models. PM₁₀ concentration dataset has been taken from Air Quality Statistics database of Turkish Statistical Institute. In PM₁₀ concentration 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 the city of Konya. In the forecasting of PM₁₀ concentration in Konya province, temperature (°C), humidity (%), pressure (kPa), and wind velocity (km/h) attributes have been used. The experimental results demonstrated that the ODDS method has obtained very promising results in the prediction of PM₁₀ concentration values.