https://scholars.lib.ntu.edu.tw/handle/123456789/637267
標題: | Deep Learning-Based Multi-Timestamp Multi-Location PM2.5 Prediction: Verification by Using a Mobile Monitoring System With an IoT Framework Deployed in the Urban Zone of a Metropolitan Area | 作者: | Chiang, Yu Lun Wang, Jen Cheng Lee, Mu Hwa Liu, An Chi JOE-AIR JIANG |
關鍵字: | Air pollution | Airbox | Atmospheric modeling | Data models | Forecasting | Gated recurrent unit | Internet of things framework | Long short-term memory | Monitoring | PM2.5 prediction | Pollution measurement | Predictive models | 公開日期: | 1-一月-2023 | 起(迄)頁: | 1-1 | 來源出版物: | IEEE Internet of Things Journal | 摘要: | The issue of air pollution in urban areas is gaining attention due to the rise of environmental and health concerns, especially for the particulate matter 2.5 (PM2.5), which poses the greatest health risk to humans. Accurate air quality prediction data allows government officials and the public to take preventive measures in advance. Recently, many air quality prediction studies have used machine learning techniques to identify patterns and rules in air quality data. However, these studies generally adopted under-represented background levels, and the prediction intervals were often in hours, which may not be suitable for residents who needed accurate air quality forecasts. Therefore, this study proposes a deep learning-based multi-timestamp multi-location PM2.5 prediction system built on two types of recurrent neural network models: long short-term memory (LSTM) and gated recurrent unit (GRU). Airbox data for the Taipei metropolitan area serves as the main source of training data to develop a forecasting model that can predict changes of PM2.5 levels within the next six to thirty minutes in different locations. The prediction results are verified by comparing them with the PM2.5 measuring results from an internet of things (IoT)-based on-vehicle monitoring system, which enables real-time data sensing and collection, and wireless transmission. The error and accuracy are 0.922 μ/m3 and 100% for the LSTM-based prediction model, and 0.940 μ/m3 and 95.7% for the GRU-based prediction model, respectively. These results can be sent out as warning messages to elderly and asthmatic patients, or serve as important information for route recommendations and policy formulation. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174815519&doi=10.1109%2fJIOT.2023.3322862&partnerID=40&md5=bcd17ee1ef3469743bba555c24af327f https://scholars.lib.ntu.edu.tw/handle/123456789/637267 |
ISSN: | 2327-4662 2372-2541 |
DOI: | 10.1109/JIOT.2023.3322862 |
顯示於: | 生物機電工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。