ENBIS-12 in Ljubljana
9 – 13 September 2012
Abstract submission: 15 January – 10 May 2012
PM10 Forecasting Using Mixture Linear Regression Models
11 September 2012, 10:40 – 11:00
- Submitted by
- Jean-Michel Poggi
- Jean-Michel Poggi (University of Paris Descartes), Bruno Portier (INSA Rouen), Michel Misiti (University of Orsay), Yves Misiti (University of Orsay)
- Clusterwise linear regression models are used for the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in three cities in Haute-Normandie, France: Rouen, Le Havre and Dieppe. The most important one, Rouen, is located at northwest of Paris, near the south side of Manche sea and is heavily industrialized. We consider monitoring stations reflecting the diversity of situations: urban background, traffic, rural and industrial stations. We have focused our attention on recent data from 2007 to 2010.
We accurately forecast the daily mean concentration by fitting a function of meteorological predictors and the average concentration measured on the previous day. The values of observed meteorological variables are used for fitting the models but the corresponding predictions are considered for the test data, leading to realistic evaluations of forecasting performances which are calculated through a leave-one-out scheme on the four years.
We discuss in this talk several methodological issues including various estimation schemes, the introduction of the deterministic predictions of meteorological or numerical models and the way to handle the forecasting at various horizons from some hours to one day ahead.
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