  Emissions of nitric oxide and nitrogen dioxide, which are named as NOx, are a
major environmental and health concern.To react to the climate crisis, the
South Korean government has strengthened NOx emission regulations. An accurate
NOx prediction model can help companies to meet their NOx emission quotas and
achieve cost savings. This study focuses on developing a model which forecasts
the amount of NOx emissions in Pohang, a heavy industrial city in South Korea
with serious air pollution problems.In this study, the Long-short term memory
(LSTM) modeling is applied to predict the amount of NOx emissions, with missing
data imputation using stochastic regression. Two parameters (i.e., time windows
and learning rates) necessary to run the LSTM model are tested and selected
using the Adam optimizer, one of the popular optimization methods in LSTM. I
found that the model that I applied achieved the acceptable prediction
performance since its Mean Absolute Scaled Error (MASE), the most important
evaluation criterion, is less than 1. This means that applying the model that I
developed in predicting future NOx emissions will perform better than a naive
prediction, a model that simply predicts them based on the last observed data
point.
