Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models

Authors:
José Luis Aznarte M.;José Manuel Benítez Sánchez;Diego Nieto Lugilde;Concepción de Linares Fernández;Consuelo Díaz de la Guardia;Francisca Alba Sánchez
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, ETSI Informatica, 18071 Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, ETSI Informatica, 18071 Granada, Granada, Spain;Department of Botany, University of Granada, Spain;Department of Botany, University of Granada, Spain;Department of Botany, University of Granada, Spain;Department of Botany, University of Granada, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

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Abstract

Forecasting airborne pollen concentrations is one of the most studied topics in aerobiology, due to its crucial application to allergology. The most used tools for this problem are single lineal regressions and autoregressive models (ARIMA). Notwithstanding, few works have used more sophisticated tools based in Artificial Intelligence, as are neural or neuro-fuzzy models. In this work, we applied some of these models to forecast olive pollen concentrations in the atmosphere of Granada (Spain). We first studied the overall performance of the selected models, then considering the data segmented into intervals (low, medium and high concentration), to test how they behave on each interval. Experimental results show an advantage of the neuro-fuzzy models against classical statistical methods, although there is still room for improvement.