A Deep Learning Model for Air Quality Forecasting Based on 1D Convolution and BiLSTM

Abstract

AbstractParticulate matter has a significantly larger impact on human health than other toxins which makes air pollution a highly serious problem. The air quality of a given region can be utilized as a primary determinant of the pollution index, as well as how well the industries and population are controlled. With the development of industries, monitoring urban air quality has become a persistent issue. At the same time, the crucial effect of air pollution on individuals’ healthiness and the environment and monitoring air quality is becoming gradually important, mainly in urban areas. Several computing methods have been studied and compared to verify the accurateness of air quality forecasting requirements to date, ranging from machine learning to deep learning. This paper introduced a deep learning air quality forecasting approach based on the convolutional bidirectional long short-term memory (CBLSTM) model for PM 2.5, which combines 1D convolution and bidirectional LSTM neural networks. The experiment findings demonstrate that the suggested approach outperforms the LSTM, CBLSTM, and CBGRU comparison models and achieves a high accuracy rate (MAE = 6.8 and RMSE = 10.2).KeywordsAir pollutionAir quality predictionBiLSTMConvolution neural networkLSTMPM 2.5