Ünlü, Kamil DemirberkAkbal, Y.Unlu, K. D.Industrial Engineering2024-07-052024-07-052022291735-14721735-263010.1007/s13762-021-03730-32-s2.0-85117250270https://doi.org/10.1007/s13762-021-03730-3https://hdl.handle.net/20.500.14411/1740Ünlü, Kamil Demirberk/0000-0002-2393-6691In this study, three different goals are pursued. Firstly, it is aimed to model particulate matter (PM) of Ankara, the capital of Turkey, by utilizing hybrid deep learning methodology. To do this, five different methodologies are proposed in which four of them are hybrid methods. Three different evaluation criteria as coefficient of determination (R-2), mean absolute error (MAE) and mean squared error (MSE) are used to compare the proposed methods. In the test set, the hybrid model which consists of feed-forward neural networks, convolution neural network and long short-term neural networks has the best performance with R-2 of 0.81, MSE of 73.07 and MAE of 5.6. Secondly, PM levels are categorized to form a prediction challenge in accordance with the World Health Organization standards. The particulate matter level is divided into two categories as being low or not, being moderate or not and being dangerous or not, it is shown that the proposed hybrid model which has the highest performance on forecasting, also worked perfectly in the classification task with accuracy of 94%. Finally, the effect of different pollutants and meteorological variables on the prediction of PM is investigated by employing ensemble machine learning methodology of random forest regression, extra tree regression and multiple linear regression. According to the results of the analysis, it is shown that the most important predictor variables of PM are its own lagged values, other pollutants, earth skin temperature and the wind speed.eninfo:eu-repo/semantics/closedAccessParticulate matterConvolution neural networksLong short-term memory neural networksFeed-forward neural networksGated recurrent neural networksHybrid neural networksArticleQ3Q219759115927WOS:000708362700001