A Study on Statistical Data Mining Algorithms for the Prediction of Ground-Level Ozone Concentration in the El Paso–Juarez Area

Bhuiyan, Al Masum; Mahmud, Suhail; Sarmin, Nusrat; Elahee, Sanjida

doi:10.1007/s41810-020-00074-2

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…In the logistic regression, we used the lasso regularization with the L 1 penalty and obtained the tuning parameter λ with cross-validation. The L 1 penalty is significant for variable selection and shrinkage because it forces some of the coefficients' estimates to be zero [38]. Table 4 demonstrates the coefficients of the predictors where Nitrogen Dioxide, Oxides of Nitrogen, Wind Speed, Resultant Wind Direction, Std.…”

Section: Resultsmentioning

confidence: 99%

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

Mahmud¹,

Ridi

Miah

et al. 2022

Atmosphere

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This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality’s monitoring stations corresponding to 2014–2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models.

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Section: Resultsmentioning

confidence: 99%

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

Mahmud¹,

Ridi

Miah

et al. 2022

Atmosphere

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“…The study presented here is an extended version of analyzing machine learning for the classification of ozone in the Paso del Norte region [25]. In this case, we explore six different machine learning models in the El Paso area, in order to classify the ozone concentration in a bi-national area.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…Machine learning-based techniques have recently risen in popularity in numerous applications, including ozone pollution modeling, prediction, and monitoring, owing to their flexibility and feature extraction capacity without the need to understand the underlying mechanisms for constructing empirical models. Machine learning methods, such as RF, SVR, Decision Tree (DT), and XGBoost, have been used to predict ozone concentration levels [33][34][35]. For example, Jiang et al adopted the RF with a large amount of feature engineering in the task of ozone prediction [36].…”

Section: Related Workmentioning

confidence: 99%

A comparison of machine learning methods for ozone pollution prediction

2023

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Precise and efficient ozone ($$\hbox {O}_{3}$$ O 3 ) concentration prediction is crucial for weather monitoring and environmental policymaking due to the harmful effects of high $$\hbox {O}_{3}$$ O 3 pollution levels on human health and ecosystems. However, the complexity of $$\hbox {O}_{3}$$ O 3 formation mechanisms in the troposphere presents a significant challenge in modeling $$\hbox {O}_{3}$$ O 3 accurately and quickly, especially in the absence of a process model. Data-driven machine-learning techniques have demonstrated promising performance in modeling air pollution, mainly when a process model is unavailable. This study evaluates the predictive performance of nineteen machine learning models for ozone pollution prediction. Specifically, we assess how incorporating features using Random Forest affects $$\hbox {O}_{3}$$ O 3 concentration prediction and investigate using time-lagged measurements to improve prediction accuracy. Air pollution and meteorological data collected at King Abdullah University of Science and Technology are used. Results show that dynamic models using time-lagged data outperform static and reduced machine learning models. Incorporating time-lagged data improves the accuracy of machine learning models by 300% and 200%, respectively, compared to static and reduced models, under RMSE metrics. And importantly, the best dynamic model with time-lagged information only requires 0.01 s, indicating its practical use. The Diebold-Mariano Test, a statistical test used to compare the forecasting accuracy of models, is also conducted.

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A Study on Statistical Data Mining Algorithms for the Prediction of Ground-Level Ozone Concentration in the El Paso–Juarez Area

Cited by 3 publications

References 22 publications

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

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

A comparison of machine learning methods for ozone pollution prediction

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