|Title:||Urban area PM 2.5 prediction with machine methods: An on-board monitoring system||Authors:||Chiang Y.-L.
|Issue Date:||2019||Start page/Pages:||25-30||Source:||International Conference on Sensing Technology||Abstract:||
Recently, particulate matter 2.5 (PM 2.5 ) has drawn more and more attention due to the pursuit of life quality. In Taiwan, PM 2.5 concentration data mostly come from the limited static stations, and their locations are far from streets where people walk or drive by. This might cause the underestimation of PM 2.5 concentration. In this paper, an on-board monitoring system is established to record the PM 2.5 concentration in the surrounding areas where people live. The recorded PM 2.5 concentration data are more accurate than the data from the static stations. In order to get the future PM 2.5 concentration trend, the prediction model is established in this study. A long short-term memory (LSTM) and a gated recurrent unit (GRU) are used as the core of the prediction model due to their ability to analyze time series data such as the PM 2.5 concentration data. And the research results show that the root mean square error (RMSE) of the prediction model using LSTM and GRU is 3.57 and 3.67 in the testing set. The prediction results can provide important air pollutant information to the public and can be used to make better air pollution control policies. © 2018 IEEE.
|ISBN:||9781538651476||DOI:||10.1109/ICSensT.2018.8603564||SDG/Keyword:||Air pollution; Air pollution control; Brain; Digital storage; Forecasting; Long short-term memory; Mean square error; gated recurrent unit; On-board monitoring systems; Particulate Matter; PM2.5; PM2.5 concentration; Prediction model; Root mean square errors; Time-series data; Monitoring
|Appears in Collections:||地理環境資源學系|
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