Prediction of hourly O3 concentrations using support vector regression algorithms

Ortiz-García, Emilio G.; Salcedo‐Sanz, Sancho; Pérez-Bellido, Ángel M.; Portilla-Figueras, Antonio; Prieto, Luís

doi:10.1016/j.atmosenv.2010.07.024

Cited by 73 publications

(27 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning models have shown good performance over a wide range of applications including that related to environmental studies [3,17]. Specifically, support vector regression algorithms (SVR) showed amazing performance on ozone short-term prediction [8]. In the fore-mentioned paper, the authors tried several configurations to obtain the best set of explicative variables to predict O 3 concentration.…”

Section: Related Workmentioning

confidence: 99%

“…[6,7]. Moreover, it is responsible for increases in mortality rates during episodes of high concentrations [8].…”

Section: Causes and Effects Of Ozone Concentrationsmentioning

confidence: 99%

“…Ozone is not directly emitted by human activities. In troposphere, it is a secondary pollutant which formation depends on a complex cycle [8]. Ozone is produced by atmospheric photochemical reactions that need solar radiation.…”

Section: Causes and Effects Of Ozone Concentrationsmentioning

confidence: 99%

“…A wide range of statistical approaches was presented in the previous studies to predict O 3 concentrations. On the one hand Ortiz-García et al [8] used principal component analysis, on the other hand Yi and Prybutok, Spellman, Gómez-Sanchis et al and Pires et al [7,[13][14][15] used artificial neural network. In a more recent work, Feng et al [16] combine neural network with support vector machines.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Ozone Concentration Forecasting Using Statistical Learning Approaches

Ishak¹,

Daoud²,

Trabelsi³

2017

JMES

View full text Add to dashboard Cite

In this paper, we are interested in the statistical modeling and forecasting of the daily maximum ozone concentration in three monitoring stations from Tunisia. A large number of explicative variables has been considered in our study. We have focused our attention on the problem of variable selection in order to improve the forecasting performance. To achieve our goal, we have used Support Vector Regression (SVR) and Random Forests (RF). The main novelties of this paper are: the variety and originality of the approaches for variable selection in regression, and the audaciousness to deal with a sticky situation characterized by a relatively big pannier of explicative variables compared to the number of observations. The experimental results demonstrate that Random Forests outperform Support Vector Regression in variable ranking and selection. Finally, it was shown that the forecasting accuracy is at least preserved, for the three stations, when using only the selected variables.

show abstract

Section: Related Workmentioning

confidence: 99%

“…[6,7]. Moreover, it is responsible for increases in mortality rates during episodes of high concentrations [8].…”

Section: Causes and Effects Of Ozone Concentrationsmentioning

confidence: 99%

Section: Causes and Effects Of Ozone Concentrationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Ozone Concentration Forecasting Using Statistical Learning Approaches

Ishak¹,

Daoud²,

Trabelsi³

2017

JMES

View full text Add to dashboard Cite

show abstract

“…Indeed, air pollution is responsible for major health effects and diseases and for increases in mortality rates (Ortiz-García et al, 2010). However, it is almost impossible to isolate pollutants but we can reduce their harmful effects by modeling and forecasting them in order to take necessary precautions.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting

Ishak¹

2016

J ENV INFORM

View full text Add to dashboard Cite

ABSTRACT. In this work, the problem of variable selection for regression is investigated in order to improve the forecasting accuracy. To that effect, the support vector regression (SVR) and the random forests (RF) are used to assess the variable importance. Then, a stepwise algorithm is built to select the best subset of predictors. An intensive comparative study is conducted on simulated and real datasets. The real datasets expose the problem of particulate matter concentration forecasting in two monitoring stations from Tunisia. We have proposed a combined approach using SVR and RF for variable importance assessment and for variable selection. We have achieved a significant improvement in forecasts accuracy for the two stations when using only a reduced number of selected predictors.

show abstract

Managing air quality: Predicting exceedances of legal limits for PM10 and O3 concentration using machine learning methods

Krylova

Okhrin

2021

Environmetrics

View full text Add to dashboard Cite

Air pollution imposes great costs on productivity, safety and health of individuals and dictates necessity of a proactive air pollution management. This, in turn, requires powerful tools for air quality modeling. In this article we develop a two-stage procedure for predicting exceedances of the EU legal limits for PM10 and O 3 concentrations using hourly data. Within the first stage we deploy machine learning methods to produce accurate 24-h-ahead forecasts of hourly pollutant concentrations at seven specific locations in the cities of Augsburg and Munich, Germany. The best performance was shown by the Stochastic Gradient Boosting Model-an ensemble tree-based method, especially convenient because of its computational efficiency and robustness to overfitting. Its predictive ability was largely superior to that reported by similar studies. In the second stage, the hourly forecasts were used to predict the exceedances of the EU daily limits for PM10 and O 3 concentrations. For both pollutants we could achieve the average probability of exceedances detection above 80%, while keeping the probability of false alarms at a reasonably low level. Such satisfactory results show that our approach can be successfully applied to anticipate the shocks, which would allow authorities to manage them in the most effective manner.

show abstract

Prediction of hourly O3 concentrations using support vector regression algorithms

Cited by 73 publications

References 37 publications

Ozone Concentration Forecasting Using Statistical Learning Approaches

Ozone Concentration Forecasting Using Statistical Learning Approaches

Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting

Managing air quality: Predicting exceedances of legal limits for PM10 and O3 concentration using machine learning methods

Contact Info

Product

Resources

About