심층 신경망을 이용한 대기질 예측 (Air Quality Prediction Using a Deep Neural Network Model)

서지정보

· 발행기관 : 한국대기환경학회
· 수록지 정보 : 한국대기환경학회지 / 35권 / 2호 / 214 ~ 225페이지
· 저자명 : 조경학, 이병영, 권명흠, 김석철

초록

A deep neural network (DNN) model of multi-layer perceptron with 3 or 4 hidden layers is developed to predict the air qualities. The DNN model takes the past 3 days of the hourly concentration measurements of the pollutants (CO, SO2, NO2, O3, PM10, PM2.5) and the meteorology data (wind speed, wind direction, air temperature, air humidity), and then predicts the hourly concentration of the pollutants for the next 24 hours. The DNN model was compared against the observations from all nationwide air quality monitoring stations which includes 115 sites in 7 metropolitan cities in South Korea. The index of agreement (IOA) was found to be 0.7~0.8, based upon the 6,505 comparison data sets from January 1, 2017 to September 30, 2017. In the unit of air quality grade, which can be evaluated from the pollutant concentration level, 60%～80% cases of the DNN predictions agree with those of the observations. For the region-wide PM10 grade, the DNN predicts exactly the 75%～85% cases of the observations, which is in about the same accuracy range of the numerical air quality models of the current operative use. Yet, for the region-wide PM2.5 grade, the cases of the accurate predictions of DNN is about twice of those of the numerical model. In the metropolitan Gwangju, for an example, the DNN predicts exactly the 211 next days of the PM2.5 grade, while the numerical model forecasts just 120 days correctly.

영어초록

A deep neural network (DNN) model of multi-layer perceptron with 3 or 4 hidden layers is developed to predict the air qualities. The DNN model takes the past 3 days of the hourly concentration measurements of the pollutants (CO, SO2, NO2, O3, PM10, PM2.5) and the meteorology data (wind speed, wind direction, air temperature, air humidity), and then predicts the hourly concentration of the pollutants for the next 24 hours. The DNN model was compared against the observations from all nationwide air quality monitoring stations which includes 115 sites in 7 metropolitan cities in South Korea. The index of agreement (IOA) was found to be 0.7~0.8, based upon the 6,505 comparison data sets from January 1, 2017 to September 30, 2017. In the unit of air quality grade, which can be evaluated from the pollutant concentration level, 60%～80% cases of the DNN predictions agree with those of the observations. For the region-wide PM10 grade, the DNN predicts exactly the 75%～85% cases of the observations, which is in about the same accuracy range of the numerical air quality models of the current operative use. Yet, for the region-wide PM2.5 grade, the cases of the accurate predictions of DNN is about twice of those of the numerical model. In the metropolitan Gwangju, for an example, the DNN predicts exactly the 211 next days of the PM2.5 grade, while the numerical model forecasts just 120 days correctly.

참고자료

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