A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
PM10 forecasting using clusterwise regression
Abstract In this paper, we are interested in the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in the city of Rouen, in Haute-Normandie, France. Located at northwest of Paris, near the south side of Manche sea and heavily industrialised. We consider three monitoring stations reflecting the diversity of situations: an urban background station, a traffic station and an industrial station near the cereal harbour of Rouen. We have focused our attention on data for the months that register higher values, from December to March, on years 2004–2009. The models are obtained from the winter days of the four seasons 2004/2005 to 2007/2008 (training data) and then the forecasting performance is evaluated on the winter days of the season 2008/2009 (test data). We show that it is possible to 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 and are also considered for the test data. We have compared the forecasts produced by three different methods: persistence, generalized additive nonlinear models and clusterwise linear regression models. This last method gives very impressive results and the end of the paper tries to analyze the reasons of such a good behavior.
Highlights ► We consider the statistical forecasting of the daily mean PM10 concentration. ► Three monitoring stations in the city of Rouen, France, are considered. ► Models use previous PM10 values and meteorological variables. ► Clusterwise linear models provide accurate forecasts and outperform two competitors. ► Such a model is detailed in one station, highlighting the reasons of such results.
PM10 forecasting using clusterwise regression
Abstract In this paper, we are interested in the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in the city of Rouen, in Haute-Normandie, France. Located at northwest of Paris, near the south side of Manche sea and heavily industrialised. We consider three monitoring stations reflecting the diversity of situations: an urban background station, a traffic station and an industrial station near the cereal harbour of Rouen. We have focused our attention on data for the months that register higher values, from December to March, on years 2004–2009. The models are obtained from the winter days of the four seasons 2004/2005 to 2007/2008 (training data) and then the forecasting performance is evaluated on the winter days of the season 2008/2009 (test data). We show that it is possible to 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 and are also considered for the test data. We have compared the forecasts produced by three different methods: persistence, generalized additive nonlinear models and clusterwise linear regression models. This last method gives very impressive results and the end of the paper tries to analyze the reasons of such a good behavior.
Highlights ► We consider the statistical forecasting of the daily mean PM10 concentration. ► Three monitoring stations in the city of Rouen, France, are considered. ► Models use previous PM10 values and meteorological variables. ► Clusterwise linear models provide accurate forecasts and outperform two competitors. ► Such a model is detailed in one station, highlighting the reasons of such results.
PM10 forecasting using clusterwise regression
Poggi, Jean-Michel (author) / Portier, Bruno (author)
Atmospheric Environment ; 45 ; 7005-7014
2011-09-09
10 pages
Article (Journal)
Electronic Resource
English
Pavement Condition Prediction Using Clusterwise Regression
| British Library Conference Proceedings | 2006
Pavement Condition Prediction Using Clusterwise Regression
| British Library Online Contents | 2006
Probabilistic Analysis of Pavement Distress Ratings with the Clusterwise Regression Method
| British Library Online Contents | 2008
| Taylor & Francis Verlag | 2020