| 摘要： | When Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2) mix, they cause pulmonary fibrosis,
and severe public health issues. Therefore, introducing deep learning models to predict changes in Nitrogen
Dioxide and Sulfur Dioxide air pollution can enable the early formulation of air pollution policies. The
study proposed an effective method for predicting air pollution levels in Taiwan by utilizing the Kalman
filtering technique and employing the seasonal gated recurrent units (SGRU) deep learning model. The
data used in this study were obtained from the Environmental Protection Administration of Taiwan. These
data include Nitrogen Dioxide and Sulfur Dioxide air pollution measurements obtained between 2005 and
2021 from various monitoring stations in Tai-wan. The proposed SGRU model was compared with six
other commonly used prediction methods, namely autoregressive integrated moving average (ARIMA),
seasonal autoregressive integrated moving average (SARIMA), exponential smoothing (ETS), Holt-Winters
exponential smoothing (HWETS), support vector regression (SVR), and seasonal long short-term memory
(SLSTM). The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used
to evaluate the accuracy of the models. The study results revealed that the SGRU model achieved the
lowest MAPE value of 0.006 for nitrogen dioxide in Shanhua, the highest value of 1.08 for Keelung, and
the lowest value of 0.005 for sulfur dioxide in Taitung, with the highest value of 2.47 for Cianjhen. This
demonstrates the broad applicability of the SGRU model to the Taiwan region. This study shows that the
SGRU model generally applies in Taiwan and has the lowest prediction error. The SGRU model can predict
air pollution levels more accurately, and this precise forecasting is crucial for governments, public health
agencies, and medical institutions to develop preventive and response measures. It helps protect plants,
animals, aquatic life, and the ecological environment while maintaining ecological balance. Incorporating air
pollution predictions into urban planning allows for early infrastructure and green energy planning, thereby
reducing air pollution levels and minimizing the resulting ecosystem degradation, promoting a healthier
urban living environment. |
