Air Quality Index Forecasting in Dhaka Using Deep Learning: A comparative study of LSTM, GRU, and BiLSTM+Conv1D

This study presents a comparative deep-learning framework for multi-step Air Quality Index (AQI) forecasting in Dhaka using LSTM, GRU, and BiLSTM+Conv1D models trained on a 25-year hourly time-series dataset. The research systematically evaluates the impact of 6 h, 12 h, and 24 h historical input windows on forecasting accuracy and demonstrates that the hybrid BiLSTM+Conv1D model achieves the best overall performance (R² = 0.9948, RMSE = 1.5773, MAE = 1.1358). The work highlights the joint importance of model architecture and temporal context for accurate urban AQI prediction and supports the development of intelligent environmental monitoring systems.