A modified PSO based hybrid deep learning approach to predict AQI of urban metropolis

Abstract

Environment and human health are seriously threatened by air pollution. The effects of air pollution are more severe in metropolitan areas due to the presence of harmful pollutants. The goal of this work is to forecast the Air Quality Index (AQI), of 15 metropolitan cities in India and analyze various air pollutants that are mostly responsible for higher levels of air pollution in a particular city. Firstly, air quality data from 15 metropolitan cities were gathered and preprocessed appropriately. The prediction models were then trained using the preprocessed dataset. Modified Particle Swarm Optimization (MPSO)-based two hybrid deep learning models: Long-Short Term Memory (LSTM) along with Bi-directional Recurrent Neural Network (BiRNN) and LSTM along with Gated Recurrent Unit (GRU) are proposed and the experimental analysis demonstrated that the proposed MPSO-LSTM-BiRNN and MPSO-LSTM-GRU models outperformed the other models' performance in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values. MPSO-LSTM-BiRNN model provides MSE, RMSE, MAE, and MAPE of 0.000184, 0.0135, 0.0088, and 27.69 % respectively whereas, the MPSO-LSTM-GRU model gives MSE, RMSE, MAE, and MAPE of 0.000188, 0.0137, 0.0091 and 26.16 % respectively.