Forecasting PM2.5 Concentration Using a Single-Dense Layer BiLSTM Method

Electronics

Md. Habibur Rahman¹  Md. Faisal Ahmed¹  Yeong Min Jang¹  Md. Morshed Alam¹  Himawan Nurcahyanto¹  Aji Teguh Prihatno¹ 

[1] Department of Electronics Engineering, Kookmin University, Seoul 02707, Korea;

关键词: PM2.5;    bidirectional long-short-term-memory (BiLSTM);    edge computing;    cleanroom;   

DOI  :  10.3390/electronics10151808

来源: DOAJ

【 摘 要 】

In recent times, particulate matter (PM2.5) is one of the most critical air quality contaminants, and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of the concentration of PM2.5 has great importance to improve the safety of the highly pollutant-sensitive electronic circuits in the factories, especially inside semiconductor industries. In this paper, a Single-Dense Layer Bidirectional Long Short-term Memory (BiLSTM) model is developed to forecast the PM2.5 concentrations in the indoor environment by using the time series data. The real-time data samples of PM2.5 concentrations were obtained by using an industrial-grade sensor based on edge computing. The proposed model provided the best results comparing with the other existing models in terms of mean absolute error, mean square error, root mean square error, and mean absolute percentage error. These results show that the low error of forecasting PM2.5 concentration in a cleanroom in a semiconductor factory using the proposed Single-Dense Layer BiLSTM method is considerably high.

【 授权许可】

Unknown