  This study uses a deep learning approach to forecast ozone concentrations
over Seoul, South Korea for 2017. We employ a deep convolutional neural network
(CNN). We apply this method to predict the hourly ozone concentration on each
day for the entire year using several predictors from the previous day,
including the wind fields, temperature, relative humidity, pressure, and
precipitation, along with in-situ ozone and NO2 concentrations. We refer to a
history of all observed parameters between 2014 and 2016 for training the
predictive models. Model-measurement comparisons for the 25 monitoring sites
for the year 2017 report average indexes of agreement (IOA) of 0.84-0.89 and a
Pearson correlation coefficient of 0.74-0.81, indicating reasonable performance
for the CNN forecasting model. Although the CNN model successfully captures
daily trends as well as yearly high and low variations of the ozone
concentrations, it notably underpredicts high ozone peaks during the summer.
The forecasting results are generally more accurate for the stations located in
the southern regions of the Han River, the result of more stable topographical
and meteorological conditions. Furthermore, through two separate daytime and
nighttime forecasts, we find that the monthly IOA of the CNN model is 0.05-0.30
higher during the daytime, resulting from the unavailability of some of the
input parameters during the nighttime. While the CNN model can predict the next
24 hours of ozone concentrations within less than a minute, we identify several
limitations of deep learning models for real-time air quality forecasting for
further improvement.
