Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

Gebreab K. Zewdie, David J. Lary, Estelle Levetin and Gemechu F. Garuma
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Gebreab K. Zewdie: William B. Hanson Center for Space Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
David J. Lary: William B. Hanson Center for Space Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
Estelle Levetin: Department of Biological Science, The University of Tulsa, Tulsa, OK 74104, USA
Gemechu F. Garuma: Institute of Earth and Environmental Sciences, University of Quebec at Montreal, Montreal, QC H2L 2C4, Canada

IJERPH, 2019, vol. 16, issue 11, 1-14

Abstract: Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

Keywords: Ambrosia pollen; random forest; extreme gradient boosting; deep neural networks; machine learning; ECMWF; pollen allergy (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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