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An Efficient Method for Capturing the High Peak Concentrations of $PM_{2.5}$ Using Gaussian-Filtered Deep Learning
| Content Provider | MDPI |
|---|---|
| Author | Yeo, Inchoon Choi, Yunsoo |
| Copyright Year | 2021 |
| Description | This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak $PM_{2.5}$ concentrations. The purpose is to accurately predict high-peak $PM_{2.5}$ concentration data that cannot be trained in general deep learning models. For the training of the proposed model, we used all available weather and air quality data for three years from 2015 to 2017 from 25 stations of the National Institute of Environmental Research (NIER) and the Korea Meteorological Administration (KMA) observatory in Seoul, South Korea. Our model trained three years of data and predicted high-peak $PM_{2.5}$ concentrations for the year 2018. In addition, we propose a Gaussian filter algorithm as a preprocessing method for capturing high concentrations of $PM_{2.5}$ in the Seoul area and predicting them more accurately. This model overcomes the limitations of conventional deep learning approaches that are unable to predict high peak $PM_{2.5}$ concentrations. Comparing model measurements at each of the 25 monitoring sites in 2018, we found that the deep learning model with a Gaussian filter achieved an index of agreement of 0.73–0.89 and a proportion of correctness of 0.89–0.96, and compared to the conventional deep learning method (average POC = 0.85), the Gaussian filter algorithm (average POC = 0.94) improved the accuracy of high-concentration $PM_{2.5}$ prediction by an average of about 9%. Applying this algorithm in the preprocessing stage could be updated to predict the risk of high $PM_{2.5}$ concentrations in real time. |
| Starting Page | 11889 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su132111889 |
| Journal | Sustainability |
| Issue Number | 21 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-27 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Marine Engineering Air-quality Gaussian Filtering Deep Learning Cnn High Peak Forecasting of Pm2.5 |
| Content Type | Text |
| Resource Type | Article |
