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Short-term PM2.5 Prediction using Modified Attention Seq2Seq BiLSTM
In semiconductor industry, concentration of particulate matter in cleanroom is important to maintain as it can damage the wafer die in the manufacturing process. The environment in semiconductor industry is well maintained where the temperature and humidity are always stable. Hence, it is not possib...
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| creator | Krishna Yoga Utama, Ida Bagus Tran, Duc Hoang Jang, Yeong Min |
| description | In semiconductor industry, concentration of particulate matter in cleanroom is important to maintain as it can damage the wafer die in the manufacturing process. The environment in semiconductor industry is well maintained where the temperature and humidity are always stable. Hence, it is not possible to use other features to predict the PM2.5 concentrations. In this paper, we present a modified attention Seq2Seq BiLSTM model for predicting the PM2.5 concentration. Based on the proposed model, short term (60 minute ahead, 90 minute ahead, and 120 minute ahead) PM2.5 concentration predictions was built. The proposed model also compared with Seq2Seq LSTM, Seq2Seq BiLSTM, and attention Seq2Seq LSTM models where the proposed model outperformed those models by achieving lowest RMSE, MAE, and MAPE values in all prediction time length. |
| doi_str_mv | 10.1109/ICUFN55119.2022.9829659 |
| format | conference_proceeding |
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The environment in semiconductor industry is well maintained where the temperature and humidity are always stable. Hence, it is not possible to use other features to predict the PM2.5 concentrations. In this paper, we present a modified attention Seq2Seq BiLSTM model for predicting the PM2.5 concentration. Based on the proposed model, short term (60 minute ahead, 90 minute ahead, and 120 minute ahead) PM2.5 concentration predictions was built. 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The environment in semiconductor industry is well maintained where the temperature and humidity are always stable. Hence, it is not possible to use other features to predict the PM2.5 concentrations. In this paper, we present a modified attention Seq2Seq BiLSTM model for predicting the PM2.5 concentration. Based on the proposed model, short term (60 minute ahead, 90 minute ahead, and 120 minute ahead) PM2.5 concentration predictions was built. 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The environment in semiconductor industry is well maintained where the temperature and humidity are always stable. Hence, it is not possible to use other features to predict the PM2.5 concentrations. In this paper, we present a modified attention Seq2Seq BiLSTM model for predicting the PM2.5 concentration. Based on the proposed model, short term (60 minute ahead, 90 minute ahead, and 120 minute ahead) PM2.5 concentration predictions was built. The proposed model also compared with Seq2Seq LSTM, Seq2Seq BiLSTM, and attention Seq2Seq LSTM models where the proposed model outperformed those models by achieving lowest RMSE, MAE, and MAPE values in all prediction time length.</abstract><pub>IEEE</pub><doi>10.1109/ICUFN55119.2022.9829659</doi><tpages>4</tpages></addata></record> |
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| identifier | EISSN: 2165-8536 |
| ispartof | 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), 2022, p.462-465 |
| issn | 2165-8536 |
| language | eng |
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| source | IEEE Xplore All Conference Series |
| subjects | Attention Seq2Seq BiLSTM Decoding Electronics industry Humidity IoT platform Manufacturing processes particulate matter PM2.5 prediction Predictive models Semiconductor device modeling |
| title | Short-term PM2.5 Prediction using Modified Attention Seq2Seq BiLSTM |
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