Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting

Pires, José C.M.; Gonçalves, B.; Azevedo, Filipa Gonçalves de; Carneiro, Aristides P.; Rego, N.; Assembleia, A. J. B.; Lima, Joselice Ferreira; Silva, Poliana; Alves, Carlos F.; Martins, F.G.

doi:10.1007/s11356-012-0829-9

Cited by 20 publications

(19 citation statements)

References 42 publications

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“…One of the most applied statistical models is the artificial neural network (ANN). ANNs are nonlinear models, which are inspired in the biological neural processing system [24,25]. These models are composed by artificial neurons (grouped in layers; three layers-input, hidden, and output-are often applied) that receive an input value and converts to an output through a selected function (activation function).…”

Section: Introductionmentioning

confidence: 99%

“…In recent studies, O 3 concentrations have shown different behaviors regarding certain explanatory variables [25,37], which can be classified as O 3 regimes. This observation can be justified by the chemical reactions associated with O 3 formation/destruction that are influenced by certain variables, such as temperature, solar radiation, and wind speed [32].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Surface Ozone Behavior at Different Regimes

Afonso

Pires

2017

Applied Sciences

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Previous studies showed that the influence of meteorological variables and concentrations of other air pollutants on O 3 concentrations changes at different O 3 concentration levels. In this study, threshold models with artificial neural networks (ANNs) were applied to characterize the O 3 behavior at an urban site (Porto, Portugal), describing the effect of environmental and meteorological variables on O 3 concentrations. ANN characteristics, and the threshold variable and value, were defined by genetic algorithms (GAs). The considered predictors were hourly average concentrations of NO, NO 2 , and O 3 , and meteorological variables (temperature, relative humidity, and wind speed) measured from January 2012 to December 2013. Seven simulations were performed and the achieved models considered wind speed (at 4.9 m·s −1 ), temperature (at 17.5 • C) and NO 2 (at 26.6 µg·m −3 ) as the variables that determine the change of O 3 behavior. All the achieved models presented a similar fitting performance: R 2 = 0.71-0.72, RMSE = 14.5-14.7 µg·m −3 , and the index of agreement of the second order of 0.91. The combined effect of these variables on O 3 concentration was also analyzed. This statistical model was shown to be a powerful tool for interpreting O 3 behavior, which is useful for defining policy strategies for human health protection concerning this air pollutant.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of Surface Ozone Behavior at Different Regimes

Afonso

Pires

2017

Applied Sciences

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“…Several studies have applied ANNs to forecast and predict the concentration of one or more air pollutants in an area, as well as its effects on the human health consequences (Gardner & Dorling, 1998;Grivas & Chaloulakou, 2006;, Pires 2012. In the present study, three types of two-layer back propagation ANNs with different training algorithms, LLR and DLLR, were applied for data modeling.…”

Section: Discussionmentioning

confidence: 99%

“…Three types of feed-forward ANNS were applied to predict O3 with 8 inputs (CO, NO2, O3, T, RH, and WS). In this study, the GA was used in order to search the numberof neurons and activation functions in hidden layers (Pires 2012). .…”

Section: Discussionmentioning

confidence: 99%

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Preprocessing and Optimization of Smooth Data-driven Model for Emergency Conditions Against Air Pollution

Ardalan¹,

Mohammadi

A³

et al. 2016

AMBIENT SCIENCE

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Ozone levels in the empty quarter of Saudi Arabia—application of adaptive neuro-fuzzy model

Rahman

Khondaker

Khan

2012

Environ Sci Pollut Res

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In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

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Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting

Abstract: In the prediction of O(3) concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.

Cited by 20 publications

References 42 publications

Characterization of Surface Ozone Behavior at Different Regimes

Characterization of Surface Ozone Behavior at Different Regimes

Preprocessing and Optimization of Smooth Data-driven Model for Emergency Conditions Against Air Pollution

Ozone levels in the empty quarter of Saudi Arabia—application of adaptive neuro-fuzzy model

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