Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Ensemble Method for Predicting Particulate Matter and Odor Intensity

미세먼지, 악취 농도 예측을 위한 앙상블 방법

  • Lee, Jong-Yeong (Dept. of Industrial and Information Systems Engineering, Jeonbuk National University) ;
  • Choi, Myoung Jin (Dept. of Defense Weapon System, Howon University) ;
  • Joo, Yeongin (Dept. of Industrial and Information Systems Engineering, Jeonbuk National University) ;
  • Yang, Jaekyung (Dept. of Industrial and Information Systems Engineering, Jeonbuk National University)
  • 이종영 (전북대학교 산업정보시스템공학과) ;
  • 최명진 (호원대학교 국방무기체계학과) ;
  • 주영인 (전북대학교 산업정보시스템공학과) ;
  • 양재경 (전북대학교 산업정보시스템공학과)
  • Received : 2019.12.16
  • Accepted : 2019.12.24
  • Published : 2019.12.31
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Abstract

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.

Keywords

References

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