Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Conformity Assessment of Machine Learning Algorithm for Particulate Matter Prediction

미세먼지 예측을 위한 기계 학습 알고리즘의 적합성 평가

  • Cho, Kyoung-woo (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH)) ;
  • Jung, Yong-jin (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH)) ;
  • Kang, Chul-gyu (Photo Team, SEMES Co., LTD.) ;
  • Oh, Chang-heon (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH))
  • Received : 2018.09.07
  • Accepted : 2018.10.01
  • Published : 2019.01.31
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Abstract

Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.

미세먼지의 인체 영향으로 인해 기존 대기 환경 모니터링 네트워크에서 측정된 과거 데이터를 활용하여 미세먼지를 예측하려는 다양한 연구가 진행되고 있다. 하지만 기존 설계된 예측 모델의 측정 환경, 세부 조건을 정확히 설정하기 어려우며, 측정된 기상 데이터의 누락과 같은 문제로 기존 연구 결과에 기반 한 새로운 예측 모델의 설계가 필요하다. 본 논문에서는 미세먼지 예측을 위한 선행 연구로서 다수의 연구에서 사용된 기계 학습 알고리즘인 다중 선형 회귀와 인공 신경망을 통해 예측 모델을 설계하여 미세먼지 예측을 위한 알고리즘의 적합성을 평가하였다. RMSE를 통한 예측 성능 비교 결과, MLR 모델의 경우 18.13, MLP 모델의 경우 14.31의 값을 보여 미세먼지 농도를 예측함에 있어 인공 신경망 모델이 예측에 더 적합함을 보였다.

Keywords

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Fig. 1 Simple Neural network example

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Fig. 2 PM10 prediction result using MLR

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Fig. 3 PM10 prediction result using ANN and MLP

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Fig. 4 MLP prediction results according to the number ofneurons

Table. 1 Parameters and algorithms of related studies

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Table. 2 RMSE comparison for each models

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