https://scholars.lib.ntu.edu.tw/handle/123456789/638105
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wu, Kuan Yen | en_US |
dc.contributor.author | Hsia, I. Wen | en_US |
dc.contributor.author | Kow, Pu Yun | en_US |
dc.contributor.author | Chang, Li Chiu | en_US |
dc.contributor.author | FI-JOHN CHANG | en_US |
dc.date.accessioned | 2023-12-25T01:54:21Z | - |
dc.date.available | 2023-12-25T01:54:21Z | - |
dc.date.issued | 2023-12-25 | - |
dc.identifier.issn | 09596526 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177760107&doi=10.1016%2fj.jclepro.2023.139825&partnerID=40&md5=14ad0789b172eaf1c689067a981877dc | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/638105 | - |
dc.description.abstract | 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. | en_US |
dc.relation.ispartof | Journal of Cleaner Production | en_US |
dc.subject | Air pollution | Autoencoder | Convolutional Neural Network (CNN) | Deep neural network (DNN) | Microsensor | Regional air quality forecasting | en_US |
dc.title | High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1016/j.jclepro.2023.139825 | - |
dc.identifier.scopus | 2-s2.0-85177760107 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85177760107 | - |
dc.relation.journalvolume | 433 | en_US |
item.openairetype | journal article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Bioenvironmental Systems Engineering | - |
crisitem.author.orcid | 0000-0002-1655-8573 | - |
crisitem.author.parentorg | College of Bioresources and Agriculture | - |
顯示於: | 生物環境系統工程學系 |
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