Journal of Artificial Intelligence and Metaheuristics

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https://doi.org/10.54216/JAIM

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Volume 1 , Issue 2 , PP: 08-16, 2022 | Cite this article as | XML | Html | PDF | Full Length Article

Improving the Regression of Air Quality Using Ensemble of Machine Learning Models

Hamzah A. Alsayadi 1 * , Abdelaziz A. Abdelhamid 2 , El-Sayed M. El-Kenawy 3 , Abdelhameed Ibrahim 4 , Marwa M. Eid 5

  • 1 Computer Science Department, Faculty of Sciences, Ibb University, Yemen - (hamzah.sayadi@cis.asu.edu.eg)
  • 2 Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt - (abdelaziz@cis.asu.edu.eg)
  • 3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt - (skenawy@ieee.org)
  • 4 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, 35516, Mansoura Egypt - (afai79@mans.edu.eg,)
  • 5 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt - (marwa.3eeed@gmail.com)
  • Doi: https://doi.org/10.54216/JAIM.010201

    Received: January 20, 2022 Accepted: May 25, 2022
    Abstract

    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.

    Keywords :

    Air quality , Ensemble model , Machine learning , Regression model

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    Cite This Article As :
    A., Hamzah. , A., Abdelaziz. , M., El-Sayed. , Ibrahim, Abdelhameed. , M., Marwa. Improving the Regression of Air Quality Using Ensemble of Machine Learning Models. Journal of Artificial Intelligence and Metaheuristics, vol. , no. , 2022, pp. 08-16. DOI: https://doi.org/10.54216/JAIM.010201
    A., H. A., A. M., E. Ibrahim, A. M., M. (2022). Improving the Regression of Air Quality Using Ensemble of Machine Learning Models. Journal of Artificial Intelligence and Metaheuristics, (), 08-16. DOI: https://doi.org/10.54216/JAIM.010201
    A., Hamzah. A., Abdelaziz. M., El-Sayed. Ibrahim, Abdelhameed. M., Marwa. Improving the Regression of Air Quality Using Ensemble of Machine Learning Models. Journal of Artificial Intelligence and Metaheuristics , no. (2022): 08-16. DOI: https://doi.org/10.54216/JAIM.010201
    A., H. , A., A. , M., E. , Ibrahim, A. , M., M. (2022) . Improving the Regression of Air Quality Using Ensemble of Machine Learning Models. Journal of Artificial Intelligence and Metaheuristics , () , 08-16 . DOI: https://doi.org/10.54216/JAIM.010201
    A. H. , A. A. , M. E. , Ibrahim A. , M. M. [2022]. Improving the Regression of Air Quality Using Ensemble of Machine Learning Models. Journal of Artificial Intelligence and Metaheuristics. (): 08-16. DOI: https://doi.org/10.54216/JAIM.010201
    A., H. A., A. M., E. Ibrahim, A. M., M. "Improving the Regression of Air Quality Using Ensemble of Machine Learning Models," Journal of Artificial Intelligence and Metaheuristics, vol. , no. , pp. 08-16, 2022. DOI: https://doi.org/10.54216/JAIM.010201