Ozone forecasting from meteorological variables

Massart, B; Kvalheim, Olav M.; Stige, Leif; Aasheim, Rune

doi:10.1016/s0169-7439(98)00064-1

Cited by 11 publications

(1 citation statement)

References 20 publications

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“…Modeling ozone series from satellite observations, past data and its relationship with meteorological variables is an important topic quite often tackled in the literature [1][2][3][4][5][6][7][8][9][10][11]. The interest in modeling ozone series started on the early 70's, when changes into the stratospheric ozone were claimed to be caused by catalytic reactions in the stratosphere that originated losses in the total amount of ozone [12,13].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary association rules for total ozone content modeling from satellite observations

Martínez-Ballesteros¹,

Salcedo‐Sanz²,

Santos³

et al. 2011

Chemometrics and Intelligent Laboratory Systems

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Section: Introductionmentioning

confidence: 99%

Evolutionary association rules for total ozone content modeling from satellite observations

Martínez-Ballesteros¹,

Salcedo‐Sanz²,

Santos³

et al. 2011

Chemometrics and Intelligent Laboratory Systems

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Support Vector Regression Algorithms in the Forecasting of Daily Maximums of Tropospheric Ozone Concentration in Madrid

Ortiz-García

Salcedo‐Sanz

Pérez-Bellido

et al. 2010

Lecture Notes in Computer Science

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Abstract. In this paper we present the application of a support vector regression algorithm to a real problem of maximum daily tropospheric ozone forecast. The support vector regression approach proposed is hybridized with an heuristic for optimal selection of hyper-parameters. The prediction of maximum daily ozone is carried out in all the station of the air quality monitoring network of Madrid. In the paper we analyze how the ozone prediction depends on meteorological variables such as solar radiation and temperature, and also we perform a comparison against the results obtained using a multi-layer perceptron neural network in the same prediction problem.

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Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City

Papanastasiou

Melas

Kioutsioukis

2007

Water Air Soil Pollut

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Suspended particulate matter is significantly related to the degradation of air quality in urban agglomerations, generating adverse health effects. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important in order to improve public awareness and air quality management. This study aims at developing models using multiple regression and neural network (NN) methods that might produce accurate 24-h predictions of daily average (DA) value of PM10 concentration and at comparatively assessing the above mentioned techniques. Pollution and meteorological data were collected in the urban area of Volos, a medium-sized coastal city in central Greece, whose population and industrialization is continuously increasing. Both models utilize five variables as inputs, which incorporate meteorology (difference between daily maximum and minimum hourly value of ground temperature and DA value of wind speed), persistency in PM10 levels and weekly and annual variation of PM10 concentration. The validation of the models revealed that NN model showed slightly better skills in forecasting PM10 concentrations, as the regression and the NN model can forecast 55 and 61% of the variance of the data, respectively. In addition, several statistical indexes were calculated in order to verify the quality and reliability of the developed models. The results showed that their skill scores are satisfying, presenting minor differences. It was also found that both are capable of predicting the exceedances of the limit value of 50 μg/m 3 at a satisfactory level.

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Ozone forecasting from meteorological variables

Cited by 11 publications

References 20 publications

Evolutionary association rules for total ozone content modeling from satellite observations

Evolutionary association rules for total ozone content modeling from satellite observations

Support Vector Regression Algorithms in the Forecasting of Daily Maximums of Tropospheric Ozone Concentration in Madrid

Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium-sized Mediterranean City

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