Machine Learning algorithms for air pollutants forecasting

Dobrea, Marius; Badicu, Andreea; Barbu, Marina; Subea, Oana; Bălănescu, Mihaela; Suciu, Geroge; Birdici, Andrei; Orza, Oana; Dobre, Ciprian

doi:10.1109/siitme50350.2020.9292238

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…They did not thoroughly discuss data handling. Dobrea et al developed a technique that calculates the number of atmospheric pollutants (PM2.5 and PM10) (Dobrea et al, 2020 ). Support Vector Regression, Autoregression Integrated Moving Average, and LSTM are the models employed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Support Vector Regression, Autoregression Integrated Moving Average, and LSTM are the models employed. After a comparison of data analysis methods and Machine Learning algorithms for estimating atmospheric pollutants (PM10 and PM2.5), it was determined that the Support Vector Regression and ARIMA (Auto Regressive Integrated Moving Average) algorithms are the most suitable for forecasting air pollutants concentrations, with correlation coefficients of 96.6% and 92.1% for PM10 and PM2.5, respectively (Dobrea et al, 2020 ). The experiment only focused on one factor of air pollution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The decision Tree was not a good time series data classifier, so it performed well in this research. Bui et al (2018) proposed a deep learning technique for air quality index predictions. The Encoder-Decoder paradigm was employed, as well as Long Short-Term Memory units.…”

Section: Attribute Descriptionmentioning

confidence: 99%

“…To the best of our knowledge, no work was done involving the analysis and prediction of air pollution in South Africa. Many have been done in countries like China (Moursi et al, 2019;Guo et al, 2020;Harishkumar et al, 2020;Balasubramanian et al, 2021;Bekkar et al, 2021;World Health Organization, 2021), India (Aditya et al, 2018;Sultana, 2019;Aarthi et al, 2020;Akiladevi et al, 2020;Masood and Ahmad, 2020), Korea (Bui et al, 2018;Xayasouk and Lee, 2018;Yang et al, 2020), and Iran (Zamani Joharestani et al, 2019). The proposed method in this study will analyze and predict the behavior of PM2.5, monitor a period of historical levels and correlation analysis for future predictions of PM2.5 levels in cities of South Africa and evaluate the models used to find the best that will be used to measure the performance of the Dataset.…”

Section: Datementioning

confidence: 99%

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Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques

Morapedi,

Obagbuwa

2023

Front. Artif. Intell.

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BackgroundAir pollution contributes to the most severe environmental and health problems due to industrial emissions and atmosphere contamination, produced by climate and traffic factors, fossil fuel combustion, and industrial characteristics. Because this is a global issue, several nations have established control of air pollution stations in various cities to monitor pollutants like Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), Carbon Monoxide (CO), Particulate Matter (PM2.5, PM10), to notify inhabitants when pollution levels surpass the quality threshold. With the rise in air pollution, it is necessary to construct models to capture data on air pollutant concentrations. Compared to other parts of the world, Africa has a scarcity of reliable air quality sensors for monitoring and predicting Particulate Matter (PM2.5). This demonstrates the possibility of extending research in air pollution control.MethodsMachine learning techniques were utilized in this study to identify air pollution in terms of time, cost, and efficiency so that different scenarios and systems may select the optimal way for their needs. To assess and forecast the behavior of Particulate Matter (PM2.5), this study presented a Machine Learning approach that includes Cat Boost Regressor, Extreme Gradient Boosting Regressor, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Decision Tree.ResultsCat Boost Regressor and Extreme Gradient Boosting Regressor were implemented to predict the latest PM2.5 concentrations for South African Cities with recording stations using past dated recordings, then the best performing model between the two is used to predict PM2.5 concentrations for South African Cities with no recording stations and also to predict future PM2.5 concentrations for South African Cities. K-Nearest Neighbor, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest Classifier were implemented to create a system predicting the Air Quality Index (AQI) Status.ConclusionThis study investigated various machine learning techniques for air pollution to analyze and predict air pollution behavior regarding air quality and air pollutants, detecting which areas are most affected in South African cities.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Attribute Descriptionmentioning

confidence: 99%