Evaluating the performance of ANN in predicting the concentrations of ambient air pollutants in Nicosia

Abstract

Air pollution is a crucial concern which impacts human health, agricultural harvests, forestry, animals as well as the environment. Indigenous environmental or health organizations regularly make daily air contamination predictions for public awareness and for use in making decisions concerning reduction methods in addition to the management air quality. Predictions are customarily based on statistical associations between meteorological conditions and ambient air contamination concentrations. Multiple linear regression (MLR) models, using similar input and output parameters, allowing a comparative study of the two methods are extensively utilized. This work aims to develop the ANN models for forecasting concentrations of PM10, NO2, and O3 in Nicosia and to compare the predictive ability of the MLR (linear method) and Artificial Neural Network (ANN) (non-linear method) models. Previous day's pollutant concentration, atmospheric pressure, wind speed, relative humidity, and temperature data from 2012 to 2015 were used as input parameters or independent variables, while observed pollutant concentration for each pollutant was used as output or dependent variable. The reliability and strength of the models were evaluated via the root mean square error, mean absolute error, and the Pearson correlation coefficient. Study results indicated that MLR did better than ANN except in a few cases. However, the Backpropagation (BP) models of all three pollutants developed in this research were found to agree with other studies in the literature proving that the BPANN models built in this study can be used for the prediction of NO2, O3, and PM10.