TL;DR
The efficiency of the proposed DNN model is proved, and the comparative analysis with the baseline models, such as Linear Regression and Support Vector Regression, demonstrates that the proposed model is more effective than traditional techniques in identifying nonlinear dependencies in air pollution data.
Pollution of air is one of the most critical environmental and public-health problems in urban regions and it is important to have predictive models to predict and give early warning. The literature survey of the paper covers all the machine learning and deep learning models used in air quality index (AQI) and concentr...
The experimental results reveal that the XGBoost model outperforms the other models, while hyperparameter tuning further improves the effectiveness of both the Decision Tree and KNN models.
Air quality degradation in rapidly urbanizing regions involves complex, non-linear dynamics that frequently elude traditional deterministic models. To tackle this limitation, this research introduces a probabilistic forecasting and transfer station methodology. By reliably extracting patterns from official sensor data,...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
