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Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port
| Content Provider | MDPI |
|---|---|
| Author | Hong, Hyunsu Jeon, Hyungjin Youn, Cheong Kim, Hyeonsoo |
| Copyright Year | 2021 |
| Description | Air pollution sources and the hazards of high particulate matter 2.5 $(PM_{2.5}$) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the $PM_{2.5}$ concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 $µg/m^{3}$ for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent $PM_{2.5}$ air pollutant-induced damage in seaports. |
| Starting Page | 1172 |
| e-ISSN | 20734433 |
| DOI | 10.3390/atmos12091172 |
| Journal | Atmosphere |
| Issue Number | 9 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-12 |
| Access Restriction | Open |
| Subject Keyword | Atmosphere Transportation Science and Technology Air Quality Pm2.5 Rnn Lstm Machine Learning Seaport |
| Content Type | Text |
| Resource Type | Article |
