• 한국멀티미디어학회논문지
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• 제22권9호
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• 한국대기환경학회지
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• 제26권6호
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.363-370
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• 2017

Data assimilation is an initializing method for air quality forecasting such as PM10. It is very important to enhance the forecasting accuracy. Optimal interpolation is one of the data assimilation techniques. It is very effective and widely used in air quality forecasting fields. The technique, however, requires too much memory space and long execution time. It makes the PM10 air quality forecasting difficult in real time. We propose a fast optimal interpolation data assimilation method for PM10 air quality forecasting using a new kernel tridiagonal sparse matrix and CUDA massively parallel processing architecture. Experimental results show the proposed method is 5~56 times faster than conventional ones.

• Communications for Statistical Applications and Methods
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• 제25권2호
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• pp.199-215
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• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1187-1198
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• 2019

In this study, we propose a new $PM_{10}$ forecasting model for Seoul region using DNN(Deep Neural Network) and secondary data. The previous numerical and Julian forecast model have been developed using primary data such as weather and air quality measurements. These models give excellent results for accuracy and false alarms, but POD is not good for the daily life usage. To solve this problem, we develop four secondary factors composed with primary data, which reflect the correlations between primary factors and high $PM_{10}$ concentrations. The proposed 4 models are A(Anomaly), BT(Back trajectory), CB(Contribution), CS(Cosine similarity), and ALL(model using all 4 secondary data). Among them, model ALL shows the best performance in all indicators, especially the PODs are improved.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1300-1312
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• 2019

BSTRACT In this paper, PM10 forecast model using DNN(Deep Neural Network) is developed for Seoul region. The previous Julian forecast model has been developed using weather and air quality data of Seoul region only. This model gives excellent results for accuracy and false alarm rates, but poor result for POD(Probability of Detection). To solve this problem, an WA(Wide Area) forecasting model that uses Chinese data is developed. The data is highly correlated with the emergence of high concentrations of PM10 in Korea. As a result, the WA model shows better accuracy, and POD improving of 3%(D+0), 21%(D+1), and 36%(D+2) for each forecast period compared with the Julian model.

• 한국대기환경학회지
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• 제31권5호
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• pp.430-436
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• 2015

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• 대기
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• 제22권2호
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• pp.245-257
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• 2012

The Asian dust (Hwangsa) forecasting model, Asian Dust Aerosol Model (ADAM) has been modified by using satelliate monitoring of surface vegetation, which enables to simulate dusts occuring not only in springtime but also for all-year-round period. Coupled with the Unified Model (UM), the operational weather forecasting model at KMA, UM-ADAM2 was implemented for operational dust forecasting since 2010, with an aid of development of Meteorology-Chemistry Interface Processor (MCIP) for usage UM. The performance analysis of the ADAM2 forecast was conducted with $PM_{10}$ concentrations observed at monitoring sites in the source regions in China and the downstream regions of Korea from March to December in 2010. It was found that the UM-ADAM2 model was able to simulate quite well Hwangsa events observed in spring and wintertime over Korea. In the downstream region of Korea, the starting and ending times of dust events were well-simulated, although the surface $PM_{10}$ concentration was slightly underestimated for some dust events. The general negative bias less than $35{\mu}g\;m^{3}$ in $PM_{10}$ is found and it is likely to be due to other fine aerosol species which is not considered in ADAM2. It is found that the correlation between observed and forecasted $PM_{10}$ concentration increases as forecasting time approaches, showing stably high correlation about 0.7 within 36 hr in forecasting time. This suggests the possibility that there is potential for the UM-ADAM2 model to be used as an operational Asian dust forecast model.

• 자원ㆍ환경경제연구
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• 제28권4호
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• pp.467-495
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• 2019

태양광 발전과 같은 신재생에너지의 불확실성은 전력계통의 유연성을 저해하며, 이를 방지하기 위해서는 정확한 발전량의 사전 예측이 중요하다. 본 연구는 미세먼지 농도를 포함한 기상자료를 이용하여 태양광 발전량을 예측하는 것을 목적으로 한다. 본 연구에서는 2016년 1월 1일부터 2018년 9월 30일까지의 발전량, 기상자료, 미세먼지 농도 자료를 이용하고 머신러닝 기반의 RBF 커널 함수를 사용한 서포트 벡터 머신을 적용하여 태양광 발전량을 예측하였다. 예측변수에 미세먼지 농도 반영 유무에 따른 태양광 발전량 예측 모델의 성능을 비교한 결과 미세먼지 농도를 반영한 발전량 예측 모델의 성능이 더 우수한 것으로 나타났다. 미세먼지를 고려한 예측 모형은 미세먼지를 고려하지 않은 예측 모형 대비 6~20시 예측 모형에서는 1.43%, 12~14시 예측 모형에서는 3.60%, 13시 예측 모형에서는 3.88%만큼 오차가 감소하였다. 특히 발전량이 많은 주간 시간대에 미세먼지 농도를 반영하는 모형의 예측 정확도가 더 뛰어난 것으로 나타났다.