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.

Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm

“…Bi-LSTM [16] Multi-density layer Bidirectional Long Short Term Memory Neural Network for predicting PM2.5 concentration in indoor environment proposed by Prihatno.…”

“…Traditional machine learning techniques such as Random Forest (RF) [12] and Extreme Gradient Booster (XGBoost) [13] have been widely applied in the field of air quality prediction. Due to the advantages of deep learning in feature extraction and mining, more and more research is focusing on recursive neural networks and their variant models such as Long Short-Term Memory Networks (LSTMs) [14], Gated Recurrent Units (GRUs) [15], Bidirectional Long Short-Term Memory Networks (Bi-LSTMs) [16], Bidirectional Gated Recurrent Units (Bi-GRUs) [17], and Convolutional Long Short-Term Memory Networks (CNN-LSTMs) [18]. Seng et al [14] proposed a multi-index comprehensive prediction model based on an LSTM, using data on the concentration of major pollutants at representative sites in Beijing, and demonstrated its effectiveness in air quality prediction.…”

“…Compared with traditional models such as ARIMA, they have stronger advantages in terms of evaluation indicators such as R 2 , MAE, and RMSE. Prihatno et al [16] proposed a multi-density layer model based on a Bi-LSTM to more accurately predict PM2.5 concentration levels in air quality pollutants. Zhang et al [17] proposed a Bi-GRU model based on spatial geographic location information.…”