Mode Decomposition Bi-Directional Long Short-Term Memory (BiLSTM) Attention Mechanism and Transformer (AMT) Model for Ozone (O 3 ) Prediction in Johannesburg, South Africa

Israel Edem Agbehadji () and Ibidun Christiana Obagbuwa ()
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Israel Edem Agbehadji: Centre for Global Change, Faculty of Natural and Applied Sciences, Sol Plaatje University, Kimberley 8301, South Africa
Ibidun Christiana Obagbuwa: Department of Computer Science and Information Technology, Faculty of Natural and Applied Sciences, Sol Plaatje University, Kimberly 8301, South Africa

Forecasting, 2025, vol. 7, issue 2, 1-19

Abstract: This paper presents a model that combines mode decomposition approaches with a bi-directional long short-term memory (BiLSTM) attention mechanism and a transformer (AMT) to predict the concentration level of ozone (O 3 ) in Johannesburg, South Africa. Johannesburg is a densely populated city and the industrial and economic hub of South Africa. Being the industrial hub, air pollution is a major concern as it affects human health. Using air pollutants and meteorological datasets, a model was proposed that uses a mode decomposition approach to address the nonlinear nature of O 3 concentration. This nonlinearity is one of the most challenging issues in air quality prediction, and this study proposed a model to decompose input data and identify the most relevant features and leverage attention mechanisms to produce weighted parameters that can enhance the model’s performance. The model’s performance enhancement approach was aimed at ensuring an effective model that easily adapts to frequently changing pollutant data in air quality prediction. The performance was evaluated statistically with root mean squared error (RMSE), mean absolute error (MAE), and mean square error (MSE). The proposed EEMD-CEEMDAN-BiLSTM-AMT model produced the most optimal result with MSE (4.80 × 10 −6 ), RMSE (0.002), and MAE (0.001). When compared with the other similar models, the proposed model was best in terms of MSE value. Future work seeks to enhance the proposed model to fine-tune its performance on different air pollutant concentrations in South Africa.

Keywords: empirical mode decomposition (EMD); enhanced empirical mode decomposition (EEMD); complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); BiLSTM; attention mechanism and transformer; ozone prediction; South Africa (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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