Air quality prediction via hybrid BiGRU-MLP using binary ant colony optimization and firefly algorithm for hyperparameter tuning

Air quality prediction is an important environmental task that supports pollution monitoring, public health protection, and sustainable urban decision-making. This study proposes a hybrid BiGRU-MLP prediction framework enhanced using Binary Ant Colony Optimization (BACO) for feature selection and the Firefly Algorithm (FA) for hyperparameter tuning. The proposed model was evaluated using the public Air Quality dataset from Kaggle, which contains 9,357 records and multiple pollutant and meteorological attributes, including CO(GT), NOx(GT), NO2(GT), temperature, relative humidity, and sensor-based air quality indicators. The dataset was divided into 70% training, 20% validation, and 10% testing subsets to ensure reliable model development and unbiased performance evaluation. Several baseline models were implemented for comparison, including BiGRU, MLP, Neural Network (NN), LSTM, and BiLSTM. The experimental results demonstrate that the proposed BiGRU-MLP model achieved the best predictive performance, obtaining an MSE of 0.0001, RMSE of 0.0101, MAE of 0.0081, MedAE of 0.0070, MAPE of 0.0008, and R² of 99.99%. In comparison, the standalone BiGRU achieved an R² of 96.87%, while MLP, NN, LSTM, and BiLSTM achieved R² values of 95.17%, 92.71%, 90.88%, and 89.29%, respectively. These results confirm that combining BiGRU's temporal learning capability with MLP's nonlinear feature representation significantly improves prediction accuracy. The integration of BACO and FA further enhances the model by selecting informative features and optimizing model parameters. Therefore, the proposed framework provides an accurate and efficient solution for air quality prediction and can support intelligent environmental monitoring systems.