The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM10) in the eastern part of Turkey

Abstract

Abstract The main purpose of this study is to select the most reliable nonlinear computational model to predict the particulate matter (PM10) concentrations. Time series data of three years PM10 concentrations were used as input variable. For the prediction, three different types of dynamic nonlinear autoregressive models were built and compared. These models are the Levenberg-Marquardt algorithm, the Bayesian Regulization algorithm, and the Scaled Conjugate Gradient algorithm. For each of these algorythms, various settings were adopted with the subsequent statistical analysis. To analyse the model performance, we used mean prediction error, root mean square error, and correlation coefficient. The lowest root mean square errors were found for the Levenberg-Marquardt algorithm with 15 neurons, for the Bayesian Regularization and for the Scaled Conjugate Gradient algorithm with 20 neurons in hidden layer. In our study, we focused on the long-term forecast of stationary dynamic time series data and on the large amount of data, which is presented as a scientific novelty. Additionally, we determined the main model parameters that most improve quality in terms of training and network capacity. Therefore, the derived forecasting model can be used as a priori for air quality management and regulations aimed on the reducing of pollutant level.