Bi-Directional Long Short-Term Memory Network (BiDLSTM) Model Based Air Pollution Prediction

Air pollution is a serious problem over the globe. It could put human life in peril by bringing on illness and occasionally even death. The purpose of air quality forecasting is to provide early warnings and minimize the consequences of air pollution. An important gauge of air quality is the concentration of small particulate matter (PM2.5), which are inhalable particles with a diameter of around 2.5 micrometers. These particles pose major health risks because they can deeply penetrate the lungs. This research investigates precise prediction of PM2.5, which may help to mitigate detrimental outcomes. Authors have proposed stacked bi-directional long short-term memory (BiDLSTM) model for forecasting. This will capture complicated temporal correlations in air quality data, this cutting-edge model makes use of LSTM neural networks' bidirectional sequence processing capabilities. The BiDLSTM model can make extremely precise forecasts about the amount of pollution in the future by examining historical air quality measurements taken in various directions in time. In this paper, air quality dataset of India is used for model performance evaluation. Along with this, LSTM model is used for comparison purpose. Different evaluation parameters like average absolute deviation, root mean square deviation and determination coefficient are calculated to quantify the model's performance. The evaluation findings suggest that BiDLSTM model performs excellently.