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.

1. 서 론
2. 기존 연구
3. 대기질 예측 방법론
    3.1 시계열 예측모델(Temporal Predictor)
    3.2 공간속성 기반 예측모델(Spatial Predictor)
    3.3 시공간 예측모델(Spatio-Temporal Predictor)
4. 실험 결과
    4.1 공간속성 기반 예측모델 분석 결과
    4.2 시공간 예측모델 분석 결과
5. 결 론
References

• Jong-Yeong Lee(전북대학교 산업정보시스템공학과) | 이종영
• Myoung Jin Choi(호원대학교 국방무기체계학과) | 최명진
• Yeongin Joo(전북대학교 산업정보시스템공학과) | 주영인
• Jaekyung Yang(전북대학교 산업정보시스템공학과) | 양재경 Corresponding Author