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

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 modelin...

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Autores principales:	Md Al Masum Bhuiyan, Ramanjit K. Sahi, Md Romyull Islam, Suhail Mahmud
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tropospheric ozone machine learning El Paso-Juarez semi-arid climate Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/fd262ba9731e4450b9b24adbc10c6d08
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spelling	oai:doaj.org-article:fd262ba9731e4450b9b24adbc10c6d082021-11-25T18:17:02ZMachine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region10.3390/math92229012227-7390https://doaj.org/article/fd262ba9731e4450b9b24adbc10c6d082021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2901https://doaj.org/toc/2227-7390In 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.Md Al Masum BhuiyanRamanjit K. SahiMd Romyull IslamSuhail MahmudMDPI AGarticletropospheric ozonemachine learningEl Paso-Juarezsemi-arid climateMathematicsQA1-939ENMathematics, Vol 9, Iss 2901, p 2901 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tropospheric ozone machine learning El Paso-Juarez semi-arid climate Mathematics QA1-939
spellingShingle	tropospheric ozone machine learning El Paso-Juarez semi-arid climate Mathematics QA1-939 Md Al Masum Bhuiyan Ramanjit K. Sahi Md Romyull Islam Suhail Mahmud Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region
description	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.
format	article
author	Md Al Masum Bhuiyan Ramanjit K. Sahi Md Romyull Islam Suhail Mahmud
author_facet	Md Al Masum Bhuiyan Ramanjit K. Sahi Md Romyull Islam Suhail Mahmud
author_sort	Md Al Masum Bhuiyan
title	Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region
title_short	Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region
title_full	Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region
title_fullStr	Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region
title_full_unstemmed	Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region
title_sort	machine learning techniques applied to predict tropospheric ozone in a semi-arid climate region
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fd262ba9731e4450b9b24adbc10c6d08
work_keys_str_mv	AT mdalmasumbhuiyan machinelearningtechniquesappliedtopredicttroposphericozoneinasemiaridclimateregion AT ramanjitksahi machinelearningtechniquesappliedtopredicttroposphericozoneinasemiaridclimateregion AT mdromyullislam machinelearningtechniquesappliedtopredicttroposphericozoneinasemiaridclimateregion AT suhailmahmud machinelearningtechniquesappliedtopredicttroposphericozoneinasemiaridclimateregion
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Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

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