Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

Abstract Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

[1]  Giorgio Corani,et al.  Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning , 2005 .

[2]  S. Sillman,et al.  Tropospheric Ozone and Photochemical Smog , 2014 .

[3]  G. Notton,et al.  A Neural Network model forecasting for prediction of hourly ozone concentration in Corsica , 2011, 2011 10th International Conference on Environment and Electrical Engineering.

[4]  Predrag Hercog,et al.  Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations , 2009 .

[5]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[6]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[7]  Roderick J. A. Little.  Regression with Missing X's: A Review , 1992 .

[8]  R. Atkinson.  Atmospheric chemistry of VOCs and NOx , 2000 .

[9]  V. Prybutok,et al.  A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.

[10]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[11]  Gabriel Ibarra-Berastegi,et al.  From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao , 2008, Environ. Model. Softw..

[12]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[13]  Yves Candau,et al.  Hourly ozone prediction for a 24-h horizon using neural networks , 2008, Environ. Model. Softw..

[14]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[15]  Guoqiang Peter Zhang,et al.  Neural Networks for Time-Series Forecasting , 2012, Handbook of Natural Computing.

[16]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[17]  Yang Zhang,et al.  Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects , 2012 .

[18]  Yang Zhang,et al.  Real-time air quality forecasting, part I: History, techniques, and current status , 2012 .