📅 23 August 2025
DOI: 10.26877/asset.v7i3.1589
Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models
Advance Sustainable Science, Engineering and Technology
Universitas Persatuan Guru Republik Indonesia Semarang
📄 Abstract
Weather monitoring is vital due to environmental changes and rising air pollution, which affects health and lifestyles. Accurate air quality prediction models are essential yet challenging due to complex weather-pollution interactions. This study employs explainable deep learning and machine learning techniques—GRU, CNN, and XGBoost—on a custom dataset of 100,000 samples with 15 features, including PM2.5, PM10, humidity, and temperature. Using SHAP for interpretability, the GRU model outperforms others with 98.56% accuracy, 98.43% Recall, and 98.52% True Positive Rate. Temperature, humidity, gases, and pressure are key variables influencing predictions. The proposed model achieves high mAP and precision, surpassing existing methods and demonstrating effective real-time forecasting under diverse weather conditions.
🔖 Keywords
#GRU-based air quality forecasting; deep learning for AQI; spatiotemporal air pollution modeling; PM 2.5 and AQI
ℹ️ Informasi Publikasi
Tanggal Publikasi
23 August 2025
Volume / Nomor / Tahun
Volume 7, Nomor 3, Tahun 2025
📝 HOW TO CITE
Kayam Saikumar; Munugapati Bhavana; Rayudu Prasanthi; Singaraju Suguna Mallika; Deepthi Kamidi; Naveen Malik; Kapil Joshi, "Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models," Advance Sustainable Science, Engineering and Technology, vol. 7, no. 3, Aug. 2025.
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