Data-driven models to forecast PM10 concentration.
ABSTRACT The research activity described in this paper concerns the study of the phenomena responsible for the urban and suburban air pollution. The analysis carries on the work already developed by the NeMeFo (neural meteo forecasting) research project for meteorological data short-term forecasting. The study analyzed the air pollution principal causes and identified the best subset of features (meteorological data and air pollutants concentrations) for each air pollutant in order to predict its medium-term concentration (in particular for the particulate matter with an aerodynamic diameter of up to 10 mum called PM10). The selection of the best subset of features was implemented by means of a backward selection algorithm which is based on the information theory notion of relative entropy. The final aim of the research is the implementation of a prognostic tool able to reduce the risk for the air pollutants concentrations to be above the alarm thresholds fixed by the law. The implementation of this tool will be carried out using data-driven models based on some of the most wide-spread statistical data-learning techniques (artificial neural networks and support vector machines).
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ABSTRACT: Hourly average concentrations of PM2.5 have been measured at a fixed point in the downtown area of Santiago, Chile. We have focused our attention on data for the months that register higher values, from May to September, on years 1994 and 1995. We show that it is possible to predict concentrations at any hour of the day, by fitting a function of the 24 hourly average concentrations measured on the previous day. We have compared the predictions produced by three different methods: multilayer neural networks, linear regression and persistence. Overall, the neural network gives the best results. Prediction errors go from 30% for early hours to 60% for late hours. In order to improve predictions, the effect of noise reduction, rearrangement of the data and explicit consideration of meteorological variables are discussed.Atmospheric Environment 01/2000; 34(8):1189-1196. · 3.11 Impact Factor