Forecasting air pollutant indicator levels with geographic models 3days in advance using neural networks

Kurt, Atakan; Oktay, Ayşe Betül

doi:10.1016/j.eswa.2010.05.093

Cited by 143 publications

(74 citation statements)

References 15 publications

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“…The statistical techniques are also used in air quality modeling studies in different part of the world (McCollister and Willson, 1975;Aron and Aron, 1978;Lin, 1982;Aron, 1984;Katsoulis, 1988;Robeson and Steyn, 1990;Milionis and Davies, 1994;Perez, 2001;Chelani et al, 2002;Nunnari et al, 2004;Kurt and Oktay, 2010). The results of statistical models are also improved after combining it with principal component analysis (PCA) (Kumar and Goyal, 2011).…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Air Dispersion Model Using Neural Network for Delhi

Kumar

Goyal

2016

Aerosol Air Qual. Res.

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Statistical downscaling methods are used to extract high resolution information from coarse resolution models. The accuracy of a modelling system in analyzing the issues of either continuous or accidental release in the atmosphere is important especially when adverse health effects are expected to be found. Forecasting of air quality levels are commonly performed with either deterministic or statistical. In this study, statistical downscaling approach is investigated for hourly PM 10 (particulate matter with aerodynamic diameter < 10 µm) pollutant for Delhi. The statistical downscaling is used on air dispersion model using neural network technique. The air dispersion model is based on analytical solution of advection diffusion equation in Neumann boundary condition for a bounded domain. Power laws are assumed for height dependent wind speed; and downwind and vertical eddy diffusivities are considered as an explicit function of downwind distance and vertical height. The predicted concentration of dispersion model with meteorological variables is used as input parameters to the neural network. It is found that performance of both air dispersion model and "pure" statistical models is inferior to that of the statistical downscaled model. In particular the root mean squares error (RMSE) of the deterministic model is reduced by at least 35% and 45% for hourly and rush hours particulate matter concentrations respectively using statistical downscaling. In addition, the results with statistical downscaled method show that the errors of the forecasts are reduced by at least 30% for stable and unstable-neutral atmospheric conditions.

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Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Air Dispersion Model Using Neural Network for Delhi

Kumar

Goyal

2016

Aerosol Air Qual. Res.

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“…Various panniers of explicative variables have been used in the previous works for the purpose of PM10 modeling and forecasting (Dong et al, 2009;Kurt and Oktay, 2010;Poggi and Portier, 2011;Domańska and Wojtylak, 2012). This variety depends on the availability of measured variables and the objectives of the study.…”

Section: Data Gathering and Descriptionmentioning

confidence: 99%

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

Ishak¹

2016

J ENV INFORM

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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.

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“…Chan andJian (2013), de Gennaro et al (2013), Cheng et al (2012), Gobakisa et al (2011), Kurt and Oktay (2010) have shown that neural networks are promising tools for air quality prediction in comparison with other statistical models like regression-based models. Moreover, ANNs, in particular the multilayer perceptron (MLP), perform better when dealing with highly nonlinear systems such as the pollution-weather phenomenon (Gardner and Dorling, 1998;Abdul-Wahab and Al-Alawi, 2008).…”

Section: Introudctionmentioning

confidence: 99%

Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network

Baawain

Al-Serihi

2014

Aerosol Air Qual. Res.

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The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM 10 , NO, NO 2 , NO x , SO 2 , H 2 S, and O 3 , which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R 2 ) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O 3 , SO 2 , and NO x , whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO 2 , and H 2 S. PM 10 concentrations are influenced by almost all the measured meteorological parameters.

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Forecasting air pollutant indicator levels with geographic models 3days in advance using neural networks

Cited by 143 publications

References 15 publications

Statistical Downscaling of Air Dispersion Model Using Neural Network for Delhi

Statistical Downscaling of Air Dispersion Model Using Neural Network for Delhi

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

Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network

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