Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling
Air pollution with suspended particles from PM2.5 fraction represents an important factor to increasing atmospheric pollution degree in urban areas, with a significant potential effect on the health of vulnerable people such as children and elderly. PM2.5
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Abstract. Air pollution with suspended particles from PM2.5 fraction represents an important factor to increasing atmospheric pollution degree in urban areas, with a significant potential effect on the health of vulnerable people such as children and elderly. PM2.5 air pollutant concentration continuous monitoring represents an efficient solution for the environment management if it is implemented as a real time forecasting system which can detect the PM2.5 air pollution trends and provide early warning or alerting to persons whose health might be affected by PM2.5 air pollution episodes. The forecasting methods for PM concentration use mainly statistical and artificial intelligence-based models. This paper presents a model based protocol, MBP – PM2.5 forecasting protocol, for the selection of the best ANN model and a case study with two artificial neural network (ANN) models for real time short-term PM2.5 forecasting. Keywords: Artificial neural networks Forecasting modeling Air pollution PM2.5 air pollutant short-term forecasting Model based forecasting protocol
1 Introduction Climate change is a modern topic nowadays. Air pollution is one of the most important environmental problems on the globe, and causes many types of allergies, respiratory illnesses, cardiovascular diseases, acute bronchitis diseases, etc. [1, 2]. Particulate matter (PM) is an air pollutant with high impact on humans because short-term and long-term exposure to high concentrations may produce severe health effects and premature mortality [3, 4]. Short-term forecasting of PM2.5 air pollution trends can use different methods: deterministic, statistical, neural, hybrid (e.g. neuro-fuzzy) etc. The statistical models include linear regression, ARIMA, principal components analysis, etc., and have been used for their forecasting skills [5, 6]. The forecasted results generated using these linear statistical models are in general not satisfactory. An alternative is the use of computational intelligence approaches, such as artificial intelligence-based models [5, 7]. Artificial neural networks [8] and adaptive neuro-fuzzy inference systems (ANFIS) have been successfully applied in air pollution forecasting domain [9–11]. The chosen of an efficient forecasting method is done by experiment, depending on the available time series databases with measurements of PM2.5 concentration, meteorological parameters, other air pollutants concentration that influence PM2.5. Depending on the © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 204–211, 2016. DOI: 10.1007/978-3-319-44944-9_18
Applying Artificial Neural Networks to Short-Term PM2.5 Forecasting Modeling
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correlation degree with PM2.5, a part of these parameters can be considered as inputs in the PM2.5 forecasting model. We are applying such a model under the ROKIDAIR research project (http://www.rokidair.ro) whose goal is to provide an
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