Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France)
Identifieur interne : 000243 ( Hal/Checkpoint ); précédent : 000242; suivant : 000244Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France)
Auteurs : Michel Misiti [France] ; Yves Misiti [France] ; Jean-Michel Poggi [France] ; Bruno Portier [France]Source :
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Abstract
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.
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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.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France)</title><author role="aut"><persName><forename type="first">Michel</forename><surname>Misiti</surname></persName><email>michel.misiti@gmail.com</email><idno type="halauthor">867003</idno><affiliation ref="#struct-40"></affiliation></author><author role="aut"><persName><forename type="first">Yves</forename><surname>Misiti</surname></persName><email>yves.misiti@math.u-psud.fr</email><idno type="halauthor">867004</idno><affiliation ref="#struct-40"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Michel</forename><surname>Poggi</surname></persName><email>jean-michel.poggi@math.u-psud.fr</email><ptr type="url" target="http://www.math.u-psud.fr/~poggi/"></ptr><idno type="halauthor">867002</idno><affiliation ref="#struct-40"></affiliation><affiliation ref="#struct-56055"></affiliation><affiliation ref="#struct-170437"></affiliation></author><author role="aut"><persName><forename type="first">Bruno</forename><surname>Portier</surname></persName><email></email><idno type="halauthor">63918</idno><affiliation ref="#struct-123600"></affiliation></author><editor role="depositor"><persName><forename>Jean-Michel</forename><surname>Poggi</surname></persName><email>jean-michel.poggi@math.u-psud.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2013-07-08 14:46:58</date><date type="whenWritten">2013-02-04</date><date type="whenModified">2016-10-11 15:00:11</date><date type="whenReleased">2013-07-09 10:27:30</date><date type="whenProduced">2013-02-04</date><date type="whenEndEmbargoed">2013-07-08</date><ref type="file" target="https://hal.archives-ouvertes.fr/hal-00841349/document"><date notBefore="2013-07-08"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.archives-ouvertes.fr/hal-00841349/file/INRIA_RR_PM10_Poggi.pdf"><date notBefore="2013-07-08"></date></ref></edition><respStmt><resp>contributor</resp><name key="185175"><persName><forename>Jean-Michel</forename><surname>Poggi</surname></persName><email>jean-michel.poggi@math.u-psud.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00841349</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-00841349</idno><idno type="halBibtex">misiti:hal-00841349</idno><idno type="halRefHtml">2013</idno><idno type="halRef">2013</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA-SACLAY">INRIA Saclay - Ile de France</idno><idno type="stamp" n="INSMI">CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions</idno><idno type="stamp" n="LM-ORSAY">Laboratoire de Mathématiques d'Orsay</idno><idno type="stamp" n="UNIV-PARIS5">Université Paris Descartes (Paris 5)</idno><idno type="stamp" n="LARA">LARA</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="USPC">Université Sorbonne Paris Cité</idno></seriesStmt><notesStmt><note type="audience" n="1">Not set</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Mixture of linear regression models for short term PM10 forecasting in Haute Normandie (France)</title><author role="aut"><persName><forename type="first">Michel</forename><surname>Misiti</surname></persName><email>michel.misiti@gmail.com</email><idno type="halAuthorId">867003</idno><affiliation ref="#struct-40"></affiliation></author><author role="aut"><persName><forename type="first">Yves</forename><surname>Misiti</surname></persName><email>yves.misiti@math.u-psud.fr</email><idno type="halAuthorId">867004</idno><affiliation ref="#struct-40"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Michel</forename><surname>Poggi</surname></persName><email>jean-michel.poggi@math.u-psud.fr</email><ptr type="url" target="http://www.math.u-psud.fr/~poggi/"></ptr><idno type="halAuthorId">867002</idno><affiliation ref="#struct-40"></affiliation><affiliation ref="#struct-56055"></affiliation><affiliation ref="#struct-170437"></affiliation></author><author role="aut"><persName><forename type="first">Bruno</forename><surname>Portier</surname></persName><idno type="halAuthorId">63918</idno><affiliation ref="#struct-123600"></affiliation></author></analytic><monogr><imprint></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Particulate matter</term><term xml:lang="en">PM10</term><term xml:lang="en">Forecasting</term><term xml:lang="en">Mixture of linear models</term><term xml:lang="en">Air quality</term></keywords><classCode scheme="classification">62M10, 62P12, 62H30</classCode><classCode scheme="halDomain" n="math.math-st">Mathematics [math]/Statistics [math.ST]</classCode><classCode scheme="halDomain" n="stat.th">Statistics [stat]/Statistics Theory [stat.TH]</classCode><classCode scheme="halTypology" n="REPORT">Reports</classCode></textClass><abstract xml:lang="en">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.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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