Short-Term Prediction of PM 2.5 Using LSTM Deep Learning Methods

Endah Kristiani, Hao Lin, Jwu-Rong Lin, Yen-Hsun Chuang, Chin-Yin Huang and Chao-Tung Yang
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Endah Kristiani: Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
Hao Lin: Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan
Jwu-Rong Lin: Department of International Business, Tunghai University, Taichung City 407224, Taiwan
Yen-Hsun Chuang: Department of International Business, Tunghai University, Taichung City 407224, Taiwan
Chin-Yin Huang: Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung City 407224, Taiwan
Chao-Tung Yang: Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan

Sustainability, 2022, vol. 14, issue 4, 1-29

Abstract: This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O 3 , SO 2 , and CO 2 from 2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model’s accuracy. The average absolute error percentage value was used in the experiments to evaluate the model’s performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value.

Keywords: PM 2.5 prediction; deep learning; air pollution; particle pollution; particulate matter forecasting; fine aerosol (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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