Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods

Arsić, Milica; Mihajlović, Ivan; Nikolić, Djordje; Živković, Živan; Panić, Marija

doi:10.1080/01919512.2019.1598844

Cited by 31 publications

(9 citation statements)

References 21 publications

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“…Multiple Linear Regression (MLR) is one of the most popular linear regression methods in predicting ozone concentration (Banja et al, 2012;Huang et al, 2019). However, this model shows drawback to apprehend the nonlinearity and complexity associated with the structure of system (Arsić et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Jumin

Zaini

Ahmed

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R 2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.

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Section: Introductionmentioning

confidence: 99%

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Jumin

Zaini

Ahmed

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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show abstract

“…Arsic, M., et al used multiple regression analysis and artificial neural network to predict ground-level ozone concentrations in the close vicinity of the city of Zrenjanin (Serbia). The comparison results show that the artificial neural network has a better effect in monitoring the ozone concentration than the multiple linear regression model [23].…”

Section: Introduction To Air Quality Prediction Modelmentioning

confidence: 99%

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Liu

Zhang²

2022

Preprint

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The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA-RVM-NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA-RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA-RVM-NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA-RVM-NAR model and other commonly used models such as support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA-RVM-NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8%~93.9%.

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“…Arsic et al used multiple regression analysis and artificial neural network to predict ground-level ozone concentrations in the close vicinity of the city of Zrenjanin (Serbia). The comparison results show that the artificial neural network has a better effect in monitoring the ozone concentration than the multiple linear regression model 23 .…”

Section: Introductionmentioning

confidence: 99%

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Liu

Zhang²

2022

Sci Rep

View full text Add to dashboard Cite

The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA–RVM–NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA–RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA–RVM–NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA–RVM–NAR model and other commonly used models such as multiple linear regression model, support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA–RVM–NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8–93.9%.

show abstract

Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods

Cited by 31 publications

References 21 publications

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

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