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Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods

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Abstract

Air quality data (observational and numerical) were used to produce hourly spot concentration forecasts of ozone (O3), particulate matter 2.5 μm (PM2.5), and nitrogen dioxide (NO2), up to 48 h for six stations across Canada—Vancouver, Edmonton, Winnipeg, Toronto, Montreal, and Halifax. Using numerical data from an air quality model (GEM-MACH15) as predictors, forecast models for pollutant concentrations were built using multiple linear regression (MLR) and multi-layer perceptron neural networks (MLPNN). A relatively new method, the extreme learning machine (ELM), was also used to overcome the limitation of linear methods as well as the large computational demand of MLPNN. In operational forecasting, the continual arrival of new data necessitates frequent model updating. This type of learning (online sequential learning) is straightforward for MLR and ELM but not for MLPNN. Forecast performance of the online sequential MLR (OSMLR) and online sequential ELM (OSELM), together with stepwise MLR, all updated daily, were compared with MLPNN updated seasonally and the benchmark climatology model. OSELM, combining relatively inexpensive frequent model updating with nonlinear modeling capability, tended to outperform the other models in mean absolute error and correlation. Compared to the linear models, the nonlinear models (OSELM and MLPNN) often had worse bias (mean error) and more severe underprediction of extreme events.

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Acknowledgments

Jonathan Baik kindly sent us the data. This research was supported by the Natural Sciences and Engineering Research Council of Canada via a Discovery Grant to W. Hsieh.

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Authors and Affiliations

  1. Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, 2207 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Huiping Peng, Aranildo R. Lima & William W. Hsieh

  2. Meteorological Service of Canada, Environment and Climate Change Canada, Queen’s Square, 45 Alderney Dr., Dartmouth, NS, B2Y 2N6, Canada

    Andrew Teakles

  3. Department of Computer Science and Technology, East China Normal University, Shanghai, 200241, China

    Jian Jin

  4. Climate Research Division, Environment and Climate Change Canada, PO Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada

    Alex J. Cannon

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  1. Huiping Peng
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  2. Aranildo R. Lima
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  3. Andrew Teakles
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  4. Jian Jin
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  5. Alex J. Cannon
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  6. William W. Hsieh
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Correspondence to William W. Hsieh.

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Peng, H., Lima, A.R., Teakles, A. et al. Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10, 195–211 (2017). https://doi.org/10.1007/s11869-016-0414-3

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  • DOI: https://doi.org/10.1007/s11869-016-0414-3

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