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Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

Author

Listed:
  • Md Al Masum Bhuiyan

    (Department of Mathematics & Statistics, Austin Peay State University, Clarksville, TN 37044, USA)

  • Ramanjit K. Sahi

    (Department of Mathematics & Statistics, Austin Peay State University, Clarksville, TN 37044, USA)

  • Md Romyull Islam

    (Department of Mathematics & Statistics, Austin Peay State University, Clarksville, TN 37044, USA)

  • Suhail Mahmud

    (Earth & Environmental Systems Institute (EESI), The Pennsylvania State University, State College, PA 16802, USA)

Abstract

In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine learning (ML) processes, based on statistical modeling, have provided a better approach to solving these risks. In this study, we compare Naive Bayes, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Extreme Gradient Boosting (XGBoost) algorithms and their ensemble technique to classify ground-level ozone concentration in the El Paso-Juarez area. As El Paso-Juarez is a non-attainment city, the concentrations of several air pollutants and meteorological parameters were analyzed. We found that the ensemble (soft voting classifier) of algorithms used in this paper provide high classification accuracy (94.55%) for the ozone dataset. Furthermore, variables that are highly responsible for the high ozone concentration such as Nitrogen Oxide (NOx), Wind Speed and Gust, and Solar radiation have been discovered.

Suggested Citation

  • Md Al Masum Bhuiyan & Ramanjit K. Sahi & Md Romyull Islam & Suhail Mahmud, 2021. "Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region," Mathematics, MDPI, vol. 9(22), pages 1-13, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2901-:d:679195
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    References listed on IDEAS

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    1. Pavelescu, Florin Marius, 2004. "Features Of The Ordinary Least Square (Ols) Method. Implications For The Estimation Methodology," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 1(2), pages 85-101, May.
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    Cited by:

    1. Monica Aureliana Petcu & Liliana Ionescu-Feleaga & Bogdan-Ștefan Ionescu & Dumitru-Florin Moise, 2023. "A Decade for the Mathematics : Bibliometric Analysis of Mathematical Modeling in Economics, Ecology, and Environment," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    2. Babek Erdebilli & Burcu Devrim-İçtenbaş, 2022. "Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey," Mathematics, MDPI, vol. 10(14), pages 1-16, July.

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