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The impact of airborne pollen on human health was recognized many years ago as high pollen concentrations of specific taxa are responsible for triggering allergic reactions to humans, therefore affecting the quality of life. In this study, we develop data-driven pollen concentration forecasting models for the city of Thessaloniki (Greece), using Artificial Neural Networks - Multi-Layer Perceptron (ANN-MLP). The data correspond to the time period 1987 - 2002 and consist of daily time-series of pollen concentrations and several meteorological parameters. We focus on the taxa of Poaceae (Grass) and Oleaceae (Olive), both known to be of high allergenicity to humans. The input variables (features) for the models were selected with the aid of a multi-objective optimization method that employed genetic algorithms. For this purpose, the number of features and the performance of the models were optimized. The resulting models indicated satisfactory performance with an Index of Agreement (IA) up to 0.93 when predicting pollen concentrations 1 day ahead, whereas the same statistical index decreases to 0.85 when the forecasting horizon is 7 days ahead, meaning that they are suitable for operational implementation.