DeepBiGRULSTM: ADVANCED DEEP LEARNING ARCHITECTURE FOR PRECISE AIR QUALITY FORECASTING AND MONITORING
DOI:
https://doi.org/10.7494/csci.2026.27.1.6073Abstract
Air pollution is a significant concern worldwide, and Bangladesh is no exception to this pressing issue. In recent times, a rising number of people have been interested in creating reliable air quality prediction models. In response, this study introduces a novel approach to predict air quality using deep learn- ing techniques, specifically Bidirectional GRU (Bi-GRU) and LSTM, using nowcast concentration and raw concentration data. To build this model, a dataset composed of historical information on air quality and the weather is taken from the US Dhaka Consulate. Through training and evaluation on a test dataset, the proposed model DeepBIGRULSTM model has been verified for its accuracy and reliability. What sets this model apart is its integration into a user-friendly mobile application, enabling easy access to daily air quality updates across Bangladesh. The results have been promising, with significantly improved accuracy. This model not only provides real-time air quality predictions but also holds the potential to issue early warnings for impending pollution events. Additionally, it aids in identifying the contributing factors behind air pollution. The innovative approach addresses the pressing issue of air pollution by providing an accessible and accurate tool to monitor and predict air quality. Its potential for early warnings and insights into pollution causes makes it a vital resource in Bangladesh’s continuous effort to improve air quality and public health.
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