Multiple regression models for predicting total daily pollen concentration in Cartagena

Abstract

The use of meteorological autocorrelation variables and pollen concentrations from previous days, coupled with classification of meteorological data according to multivariate analysis techniques, is shown to improve the predictive power of multiple regression models for daily pollen forecasts. This paper presents an investigation of the meteorological and autocorrelation variables which influence pollen counts in Cartagena, from 1995 to 1999, as a basis for the development of predictive models. The analysis of total pollen concentrations, and especially Chenopodiaceae‐Amaranthaceae, was determined. Initially, forecasting models for total pollen counts were developed, using data from 1995 to 1998, and autocorrelation and meteorological variables. Secondly, predictive models were developed for different meteorological situations, which improved the results by decreasing the number of predictive parameters. Finally, data from 1999 were used to validate the predictive models.