미세먼지, 악취 농도 예측을 위한 앙상블 방법

• 영문명: Ensemble Method for Predicting Particulate Matter and Odor Intensity
• 발행기관: 한국산업경영시스템학회
• 저자명: 이종영(Jong-Yeong Lee) 최명진(Myoung Jin Choi) 주영인(Yeongin Joo) 양재경(Jaekyung Yang)
• 간행물 정보: 『산업경영시스템학회지』제42권 제4호, 203~210쪽, 전체 8쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2019.12.30

영문 초록

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.

목차

1. 서 론

2. 기존 연구

3. 대기질 예측 방법론

4. 실험 결과

5. 결 론

키워드

Particulate Matter Odor Machine Learning Ensemble Prediction