Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas DOI Creative Commons
Cosmina-Mihaela Roșca, Mădălina Cărbureanu, Adrian Stancu

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4390 - 4390

Опубликована: Апрель 16, 2025

Air quality (AQ) is one of the most important urban environment indicators for life. The paper proposes a software solution predicting and forecasting air index (AQI) in areas. study integrates pollutant factors (CO, NO2, SO2, PM2.5), meteorological parameters (temperature, humidity, wind speed), traffic data to determine quality. For this purpose, 19 predictive models were developed compared: 12 machine learning algorithms, 7 deep learning, 1 model based on structural component analysis. Random Forest Regression model, customized within study, achieved best results, with an R2 score 99.59%, MAE 0.22%, MAPE 0.68%, OP (Overall Precision) 95.61%. It was subsequently validated unseen recorded mean deviation 0.58%. short-term AQI (5 days), AQIF 71.62%, 0.4%, 0.9%. proposed integrated into web application IoT infrastructure real-time alert mechanisms. Future directions include expanding dataset optimizing hyperparameters increase accuracy, as well integrating PM10 O3 factors, along degree industrialization demographic level.

Язык: Английский

Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas DOI Creative Commons
Cosmina-Mihaela Roșca, Mădălina Cărbureanu, Adrian Stancu

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4390 - 4390

Опубликована: Апрель 16, 2025

Air quality (AQ) is one of the most important urban environment indicators for life. The paper proposes a software solution predicting and forecasting air index (AQI) in areas. study integrates pollutant factors (CO, NO2, SO2, PM2.5), meteorological parameters (temperature, humidity, wind speed), traffic data to determine quality. For this purpose, 19 predictive models were developed compared: 12 machine learning algorithms, 7 deep learning, 1 model based on structural component analysis. Random Forest Regression model, customized within study, achieved best results, with an R2 score 99.59%, MAE 0.22%, MAPE 0.68%, OP (Overall Precision) 95.61%. It was subsequently validated unseen recorded mean deviation 0.58%. short-term AQI (5 days), AQIF 71.62%, 0.4%, 0.9%. proposed integrated into web application IoT infrastructure real-time alert mechanisms. Future directions include expanding dataset optimizing hyperparameters increase accuracy, as well integrating PM10 O3 factors, along degree industrialization demographic level.

Язык: Английский

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