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

Md Al Masum Bhuiyan, Ramanjit K. Sahi, Md Romyull Islam and Suhail Mahmud
Additional contact information
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

Mathematics, 2021, vol. 9, issue 22, 1-13

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.

Keywords: tropospheric ozone; machine learning; El Paso-Juarez; semi-arid climate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/22/2901/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/22/2901/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:22:p:2901-:d:679195

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().