Application of Long-short Term Memory (LSTM) Model for Forecasting NOx Emission in Pohang Area

Abstract

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