An Air Pollution Prediction Scheme Using Long Short Term Memory Neural Network Model

In order to establish countermeasures for air pollution, it is first necessary to accurately grasp the air pollution state and predict the cause and change trend of the pollution situation. Due to the continuously strengthening regulations on the emissions of environmental pollutants, the forecasting and management of nitrogen oxides (NOx) emissions is receiving a lot of attention from industrial sites. In this study, a model for predicting nitrogen oxide emissions based on artificial intelligence was proposed. The proposed model includes everything from data preprocessing to learning and evaluation of the model, and used a Long ShortTerm Memory (LSTM) neural network model, one of the recurrent neural networks, to predict NOx emissions with time-series characteristics. The optimized LSTM model showed more than 93% NOx emissions prediction accuracy for both the training data and the evaluation data. The model proposed in this study is expected to be applied to the development of a model for predicting the emission of various air pollutants with time-series characteristics