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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Kayam Saikumar
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Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models Kayam Saikumar; Munugapati Bhavana; Rayudu Prasanthi; Singaraju Suguna Mallika; Deepthi Kamidi; Naveen Malik; Kapil Joshi
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1589

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.
Co-Authors Deepthi Kamidi Kapil Joshi Munugapati Bhavana Naveen Malik Rayudu Prasanthi Singaraju Suguna Mallika