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Analysis of Input Factors and Performance Improvement of DNN PM2.5 Forecasting Model Using Layer-wise Relevance Propagation

계층 연관성 전파를 이용한 DNN PM2.5 예보모델의 입력인자 분석 및 성능개선

  • Yu, SukHyun (Dept. of Information, Electrical & Electronic Eng., Anyang University)
  • 유숙현
  • Received : 2021.09.23
  • Accepted : 2021.10.20
  • Published : 2021.10.30

Abstract

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM2.5, CO, and NO2 is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

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