Medium-Term AQI Prediction in Selected Areas of Bangladesh Based on Bidirectional GRU Network Model

Currently, Bangladesh is among the countries with the highest levels of air pollution in the world. Major cities of Bangladesh experience severe air pollution, significantly impacting public health and the environment. The Air Quality Index (AQI) serves as a convenient measure to assess air quality in specific locations. Accurate AQI forecasting is crucial as it allows policymakers to prepare and implement strategies to mitigate air pollution, thereby safeguarding human health and the ecosystem. The primary aim of this paper is to develop an effective model capable of forecasting AQI on a medium-term basis for four selected locations in Bangladesh. To achieve this, we propose a Bidirectional Gated Recurrent Unit (BiGRU)-based model to forecast the AQI for each location up to seven days in advance (i.e., medium-term AQI prediction). We collected daily AQI data from four distinct locations in Bangladesh from March 1, 2014, to December 31, 2020. To validate the performance of the model, we used various statistical measures, including Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination ( \(R^2\) $R^2$ ) score. We also compared the performance of the proposed model with three related models: Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Convolutional Neural Network (CNN). Simulation results demonstrate that the BiGRU model outperforms other approaches, showcasing an RMSE ranging from 27.42 to 43.27 for medium-term AQI prediction.