Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models
Year: 2025
Type: article
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.
Authors: Kayam Saikumar, Munugapati Bhavana, Rayudu Prasanthi, Singaraju Suguna Mallika, Deepthi Kamidi, Naveen Malik, Kapil Joshi
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4413421707