oneM2M-Enabled Prediction of High Particulate Matter Data Based on Multi-Dense Layer BiLSTM Model

Abstract

High particulate matter (PM) concentrations in the cleanroom semiconductor factory have become a significant concern as they can damage electronic devices during the manufacturing process. PM can be predicted before becoming more concentrated based on its historical data to support factory management in regulating the air quality in the cleanroom. In this paper, a Multi-Dense Layer BiLSTM model is proposed to predict PM2.5 concentrations in the indoor environment of the cleanroom. To obtain reliability, validity, and interoperability data, the datasets containing temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10 were retrieved in a standardized manner via oneM2M-defined representational state transfer application programmable interfaces by employing software platforms compliant with the Internet of Things (IoT) standard. Based on the proposed model, an algorithm was built providing short-term PM2.5 concentration predictions (one hour ahead, two hours ahead, and three hours ahead). The proposed model outperformed the RNN, LSTM, CNN-LSTM, and Single-Dense Layer BiLSTM models in terms of MSE, MAE, and MAPE values. The model created in this study could predict high PM2.5 concentration levels more accurately, thus providing vital support for operation and maintenance for the semiconductor industry.