Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Design of a 1-D CRNN Model for Prediction of Fine Dust Risk Level

미세먼지 위험 단계 예측을 위한 1-D CRNN 모델 설계

  • Lee, Ki-Hyeok (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Hwang, Woo-Sung (Department of Electronic, Electrical, Control & Instrumentation Engineering, Hanyang University) ;
  • Choi, Myung-Ryul (Division of Electronics Engineering, Hanyang University)
  • 이기혁 (한양대학교 전자공학과) ;
  • 황우성 (한양대학교 전기전자제어계측공학과) ;
  • 최명렬 (한양대학교 전자공학부)
  • Received : 2020.11.15
  • Accepted : 2021.02.20
  • Published : 2021.02.28
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Abstract

In order to reduce the harmful effects on the human body caused by the recent increase in the generation of fine dust in Korea, there is a need for technology to help predict the level of fine dust and take precautions. In this paper, we propose a 1D Convolutional-Recurrent Neural Network (1-D CRNN) model to predict the level of fine dust in Korea. The proposed model is a structure that combines the CNN and the RNN, and uses domestic and foreign fine dust, wind direction, and wind speed data for data prediction. The proposed model achieved an accuracy of about 76%(Partial up to 84%). The proposed model aims to data prediction model for time series data sets that need to consider various data in the future.

최근 국내 미세먼지 발생의 증가에 따라 발생하는 인체에 유해한 영향을 줄이기 위하여, 미세먼지 수치를 예측하고 사전 조치를 취할 수 있도록 돕는 기술이 필요해지고 있다. 본 논문에서는 국내 미세먼지 위험 수준을 예측하기 위한 1D Convolutional to Recurrent Neural Network (1-D CRNN) 모델을 제안한다. 제안 된 모델은 딥러닝 신경망의 CNN과 RNN을 결합한 구조이며, 다른 종류의 데이터로 구성된 시계열 데이터 세트에서 데이터 예측을 수행 할 수 있다. 데이터 예측을 위해 국내·외 미세먼지, 풍향, 풍속 데이터를 사용한다. 제안된 모델은 약 76%(부분 최대 84%)의 정확도를 달성했으며, 일반 RNN 모델(53%)보다 정확한 예측 결과를 얻었을 수 있었다. 제안된 모델은 향후 여러 개의 시계열 데이터 세트를 고려해야 하는 데이터 예측 모델 학습 및 실험을 목표로 한다.

Keywords

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