A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Junbeom Park and Seongju Chang
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Junbeom Park: Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Deajeon 34141, Korea
Seongju Chang: Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Deajeon 34141, Korea

IJERPH, 2021, vol. 18, issue 13, 1-15

Abstract: Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM 2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

Keywords: air pollution; artificial neural network; long short-term memory; fine particulate matter; prediction model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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