Real-time 7-day forecast of pollen counts using a deep convolutional neural network

Abstract

Several studies have used regression analyses to forecast pollen concentrations, yet few have applied a deep neural network in their research. This study implements a deep convolutional neural network with the great potential to recognize patterns of pollen phenomena that enable the prediction of pollen concentrations. We train the model using data from 2009 to 2015 from multiple meteorological datasets, satellite data and processed data reflecting pollen flux as input for the model. The model forecasts pollen counts 1–7 days ahead for the entire year of 2016. Comparison of daily forecasts to observations, the algorithm obtains a relatively high index of agreement and Pearson correlation coefficient of up to 0.90 and 0.88, respectively. An evaluation of categorical statistics based on defined threshold levels shows satisfactory results. Critical success index of the model forecasts is as high as 0.887 for weed pollen, 0.646 for tree pollen, and 0.294 for grass pollen. Forecasts of grass pollen exhibit the largest decrease in accuracy because of the strong variance in annual pollen concentrations. Forecasts of weed pollen exhibit the greatest consistency, with a 7-day forecast correlation and index of agreement of 0.82 and 0.77, respectively, during the peak season. This correlates with the consistency of annual and seasonal trends of weed pollen within the study area. Compared to the conventional modeling approaches, convolutional neural network shows a promising ability to predict pollen for multiple days to allow individuals with allergies to take proper precautions during high pollen days.