Advanced Trans-BiGRU-QA Fusion Model for Atmospheric Mercury Prediction

Dong-Her Shih, Feng-I. Chung, Ting-Wei Wu (), Bo-Hao Wang and Ming-Hung Shih
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Dong-Her Shih: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Feng-I. Chung: Center for General Education, National Chung Cheng University, Chiayi 621301, Taiwan
Ting-Wei Wu: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Bo-Hao Wang: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Ming-Hung Shih: Department of Electrical and Computer Engineering, Iowa State University, 2520 Osborn Drive, Ames, IA 50011, USA

Mathematics, 2024, vol. 12, issue 22, 1-34

Abstract: With the deepening of the Industrial Revolution and the rapid development of the chemical industry, the large-scale emissions of corrosive dust and gases from numerous factories have become a significant source of air pollution. Mercury in the atmosphere, identified by the United Nations Environment Programme (UNEP) as one of the globally concerning air pollutants, has been proven to pose a threat to the human environment with potential carcinogenic risks. Therefore, accurately predicting atmospheric mercury concentration is of critical importance. This study proposes a novel advanced model—the Trans-BiGRU-QA hybrid—designed to predict the atmospheric mercury concentration accurately. Methodology includes feature engineering techniques to extract relevant features and applies a sliding window technique for time series data preprocessing. Furthermore, the proposed Trans-BiGRU-QA model is compared to other deep learning models, such as GRU, LSTM, RNN, Transformer, BiGRU, and Trans-BiGRU. This study utilizes air quality data from Vietnam to train and test the models, evaluating their performance in predicting atmospheric mercury concentration. The results show that the Trans-BiGRU-QA model performed exceptionally well in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R 2 ), demonstrating high accuracy and robustness. Compared to other deep learning models, the Trans-BiGRU-QA model exhibited significant advantages, indicating its broad potential for application in environmental pollution prediction.

Keywords: atmospheric mercury; air pollution; transformer; bidirectional gated recurrent unit (BiGRU); quick attention (QA) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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