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http://dx.doi.org/10.9717/kmms.2021.24.8.1122

Analysis of Input Factors of DNN Forecasting Model Using Layer-wise Relevance Propagation of Neural Network  

Yu, SukHyun (Dept. of Information, Electrical & Electronic Eng., Anyang University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.8, 2021 , pp. 1122-1137 More about this Journal
Abstract
PM2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.
Keywords
Layer-wise Relevance Propagation (LRP); Explainable Artificial Intelligence (XAI); $PM_{2.5}$ Forecasting Model; Deep Neural Network (DNN);
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