Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France)
type: Dataset
description: Mixture of linear regression models is used for the short-term 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 Haute-Normandie region is located at northwest of Paris, near the south side of Manche sea and is heavily industrialized. We consider six 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 2011. We forecast the daily mean PM10 concentration by modeling it as a mixture of linear regression models involving 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 paper several methodological issues including estimation schemes, introduction of the deterministic predictions of meteorological models and how to handle the forecasting at various horizons from some hours to one day ahead.
landing page: https://hal.science/hal-00841349
keyword: 62m10-62p12-62h30
keyword: air-quality
keyword: forecasting
keyword: infoeu-reposemanticsreport
keyword: mathmath-stmathematics-mathstatistics-mathst
keyword: mixture-of-linear-models
keyword: particulate-matter
keyword: pm10
keyword: reports
keyword: statthstatistics-statstatistics-theory-statth
issued: 2026-05-10T11:27:07.722507
modified: 2026-05-10T11:27:07.722511
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France)
type: Distribution
format: HTML
access URL: https://hal.science/hal-00841349
issued: 2026-05-10T11:27:07.738204
modified: 2026-05-10T11:27:07.709153