Forecasting of Air Quality Using an Optimized Recurrent Neural Network

Abstract

Clean air is necessary for leading a healthy life. Many respiratory illnesses have their root in the poor quality of air across regions. Due to the tremendous impact of air quality on people’s lives, it is essential to devise a mechanism through which air pollutants (PM2.5, NOx, COx, SOx) can be forecasted. However, forecasting air quality and its pollutants is complicated as air quality depends on several factors such as weather, vehicular, and power plant emissions. This aim of this research was to find the impact of weather on PM2.5 concentrations and to forecast the daily and hourly PM2.5 concentration for the next 30 days and 72 h in Pakistan. This forecasting was done through state-of-the-art deep learning and machine learning models such as FbProphet, LSTM, and LSTM encoder–decoder. This research also successfully forecasted the proposed daily and hourly PM2.5 concentration. The LSTM encoder–decoder had the best performance and successfully forecasted PM2.5 concentration with a mean absolute percentage error (MAPE) of 28.2%, 15.07%, and 42.1% daily, and 11.75%, 9.5%, and 7.4% hourly for different cities in Pakistan. This research proves that a data-driven approach is essential for resolving air pollution in Pakistan.