Volume 1 , Issue 2 , PP: 08-16, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Hamzah A. Alsayadi 1 * , Abdelaziz A. Abdelhamid 2 , El-Sayed M. El-Kenawy 3 , Abdelhameed Ibrahim 4 , Marwa M. Eid 5
Doi: https://doi.org/10.54216/JAIM.010201
Air pollution is a particularly important problem in most countries right now because of its terrible effects on
both the environment and human health. Big cities are most impacted because of the country’s quick industrial
and economic development. In this paper, the authors proposed various regression model for the prediction of
air quality including decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors
regressor. The air quality dataset, in Itally cities, is used for training and evaluation the proposed model. The
results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to
the traditional methods.
Air quality , Ensemble model , Machine learning , Regression model
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