期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2023-11-20 |
| 作品名稱 | High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data |
| 作品名稱（其他語言） | |
| 著者 | Wu, Kuan-Yen; Hsia, I-Wen; Kow, Pu-Yun;Chang, Li-chiu; Chang, Fi-John |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of Cleaner Production 433(25), 139825 |
| 摘要 | High-resolution real-time air quality forecasting can alert decision-makers and residents about forthcoming air pollution events and refine air quality management. The Environmental Protection Administration in Taiwan has deployed numerous low-cost air quality microsensors near industrial zones lately to facilitate local air quality monitoring. Nevertheless, the frequent occurrence of missing sensor data due to problems of mobile transmission, frontend/backend device malfunction, or other unforeseen issues would raise difficulty in making quick responses to air pollution incidents. This study proposed a hybrid deep learning model (AE-CNN-BP) collaborating an Autoencoder (AE), a Convolutional Neural Network (CNN), and a Back Propagation Neural Network (BPNN) to effectively extract crucial features from big data for making successive high-spatiotemporal-resolution forecasts of PM2.5 concentrations 4 h ahead. The proposed model was trained and tested in three industrial zones densely installed with microsensors in Kaohsiung City of Taiwan. A high pollution incident was selected to evaluate model performance. The results show that the proposed model could reliably produce nice high-spatiotemporal-resolution forecasts for 12 air quality monitoring stations and 485 microsensors, with Coefficient of Determination (R2) values and Root Mean Squared Error (RMSE) of 0.82 (0.76) and 11.05 (12.75) μg/m3 in the training (testing) stage, respectively. For the selected incident, the Mean Absolute Percentage Error (MAPE) values of the proposed model were 22.3% and 27.1% at T+1 and T+4, respectively. This study demonstrates that the proposed deep learning model based on ensemble datasets of sparsely distributed monitoring stations and densely deployed microsensors can offer reliable high-spatiotemporal-resolution air quality forecasts, benefiting environmental studies and informed policymaking by accounting for local-scale variations in PM2.5 concentrations. |
| 關鍵字 | |
| 語言 | en |
| ISSN | 1879-1786 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | Chang, Li-Chiu; Chang, Fi-John |
| 審稿制度 | 是 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125207 ) |