Predicting PM2.5 concentrations in Ba Ria - Vung Tau province using an artificial neural network model

Abstract

Abstract Air pollution has increased rapidly and significantly recently, particularly in big cities. Various methods for predicting air pollution are available, including traditional air quality models, statistical techniques, and artificial intelligence. In this study, the author developed a model using a Feed Forward Neural Network with multivariate statistical methods to predict air pollution. Data from three automated air monitoring locations in Ba Ria-Vung Tau province were gathered between 2020 and 2022 to forecast the concentration of PM2.5. The results demonstrated that the FFNN model with an I(6)-HL(5)-O(1) structure outperformed other models in predicting PM2.5 concentration. The training, validation, and testing phases yielded mean squared error values of 9.2×10−6, 8.2×10−6, and 8.6×10−6, respectively. The regression coefficient obtained consistently high values across a range of experiments (above 0.99). The MSE value of the FFNN model of the prediction set was lower than that of the NSE value, which was higher than those obtained from the multiple linear regression.