Estimation of PM10 levels using feed forward neural networks in Igdir, Turkey

Abstract In areas with high air pollution, it is important to estimate the PM10 value causing this. In this way, it can be determined which input variables the PM10 value depends on and steps can be taken. Preventive measures can be taken by the public authority for the factors causing air pollution. The aim of this paper is to introduce the air pollution that has reached the threatening dimensions of human health today and to determine the effect of these elements by specifying the environmental conditions affecting it. In this study, we showed the effect of NOx and NOx concentrations on PM10 together with SO2. Also here, the effects of 9 different environmental factors (Nitrogen oxides (NOx), Sulfur dioxide (SO2), Nitrogen monoxide (NO), Nitrogen dioxide (NO2), Ozone (O3), Air temperature, Air humidity, Air pressure, and Wind speed) on PM10 value were investigated. For this reason, PM10 value was estimated by using these factors as input in the feed forward neural network. For this purpose, four-layered feed forward neural network architectures are used. 8 different experiments were done according to different layer sizes. In addition, experiments were carried out by reducing the size of the features with Principal Component Analysis (PCA) to see the results of a small number of input parameters. Five months of data obtained daily from public sources were used as a data set to conduct the experiments. Mean absolute error (MAE), correlation coefficient (r), root mean square error (RMSE) and R2 were used as performance metrics. MAE, r, RMSE and R2 were found to be 27.09, 0.95, 27.89 and 0.881, respectively. These results are quite satisfactory despite low data.