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

Amira A Mahmoud¹, Sarah M Alhammad², Yasser Fouad³

¹Department of Computer Science, Future Higher Institute for Specialized Technological Studies, Cairo, Egypt.

Scientific Reports

|June 24, 2026

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This summary is machine-generated.

This study introduces an advanced air quality prediction model using BiGRU-MLP, optimized with Binary Ant Colony Optimization and Firefly Algorithm. The hybrid approach significantly improves prediction accuracy for environmental monitoring and public health.

Area of Science:

Environmental Science
Computer Science
Data Science

Background:

Accurate air quality prediction is crucial for environmental monitoring, public health, and urban planning.
Existing models often struggle with complex pollutant dynamics and parameter optimization.

Purpose of the Study:

To develop a novel hybrid BiGRU-MLP framework for enhanced air quality prediction.
To improve model performance through optimized feature selection and hyperparameter tuning.

Main Methods:

A hybrid BiGRU-MLP model was developed for air quality prediction.
Binary Ant Colony Optimization (BACO) was used for feature selection.
Firefly Algorithm (FA) was employed for hyperparameter tuning.
The model was trained and validated on a public Kaggle Air Quality dataset.

Keywords:

Air quality prediction BiGRU-MLP model Binary ant colony optimization Environmental monitoring Feature selection Firefly algorithm Hybrid deep learning Hyperparameter tuning Regression modeling

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Main Results:

The proposed BiGRU-MLP model achieved superior predictive performance with an R² of 99.99%.
Key performance metrics included MSE of 0.0001, RMSE of 0.0101, MAE of 0.0081, MedAE of 0.0070, and MAPE of 0.0008.
The hybrid model significantly outperformed baseline models like standalone BiGRU, MLP, NN, LSTM, and BiLSTM.

Conclusions:

The combination of BiGRU and MLP effectively captures temporal and nonlinear features for accurate air quality prediction.
Feature selection and hyperparameter optimization using BACO and FA further enhance predictive capabilities.
The developed framework offers an efficient and accurate solution for intelligent environmental monitoring systems.