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Emissions of nitric oxide and nitrogen dioxide, which are named as NOx, are amajor environmental and health concern.To react to the climate crisis, theSouth Korean government has strengthened NOx emission regulations. An accurateNOx prediction model can help companies to meet their NOx emission quotas andachieve cost savings. This study focuses on developing a model which forecaststhe amount of NOx emissions in Pohang, a heavy industrial city in South Koreawith serious air pollution problems.In this study, the Long-short term memory(LSTM) modeling is applied to predict the amount of NOx emissions, with missingdata imputation using stochastic regression. Two parameters (i.e., time windowsand learning rates) necessary to run the LSTM model are tested and selectedusing the Adam optimizer, one of the popular optimization methods in LSTM. Ifound that the model that I applied achieved the acceptable predictionperformance since its Mean Absolute Scaled Error (MASE), the most importantevaluation criterion, is less than 1. This means that applying the model that Ideveloped in predicting future NOx emissions will perform better than a naiveprediction, a model that simply predicts them based on the last observed datapoint.

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