June 23, 2025Advance Sustainable Science Engineering and TechnologyOpen Access

Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models

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Authors

KSK. SaikumarGuru Nanak Institutions MBMunugapati BhavanaJawaharlal Nehru Technological University, Hyderabad RPRayudu PrasanthiAditya Birla (India)

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Implication

This analysis demonstrates that the GRU model achieves 98.56% accuracy in air quality prediction, highlighting the effectiveness of machine learning techniques for environmental monitoring.

Key Points

The GRU model attained an accuracy of 98.56% in predicting air quality, outperforming CNN and XGBoost.
Using SHAP for interpretability, the study emphasizes key variables like temperature and humidity in predictions.
The analysis employed a dataset of 100,000 samples, including critical features such as PM2.5 and PM10.
Real-time forecasting capabilities are enhanced with high precision under various weather conditions.

Cite This Study

Saikumar et al. (2025) studied this question.

synapsesocial.com/papers/68af540fad7bf08b1eadb1f8 https://doi.org/10.26877/asset.v7i3.1589

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Implication

This analysis demonstrates that the GRU model achieves 98.56% accuracy in air quality prediction, highlighting the effectiveness of machine learning techniques for environmental monitoring.

Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models

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Implication

Key Points

Cite This Study

Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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