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The Air Quality Index (AQI) is a crucial indicator for assessing the degree of atmospheric pollution. Accurately forecasting AQI is notably challenging due to the unpredictable weather conditions and the intricate interactions among various pollutants. To this end, we propose a AQI prediction framework, entitled ADNNet , to develop a streamlined attention-based method for AQI prediction. Specifically, it includes a deep neural network architecture that exclusively integrates multi-layer perceptron (MLP) and attention mechanism modules. This approach involves a novel design paradigm, where data information is first fed into the MLP layer to learn global features, which are then refined by the attention module to capture local features such as time delays and periodicity. Additionally, we incorporate a Bayesian hyperparameter optimization algorithm to make the trade-off between computational time and prediction accuracy efficient. To further enhance model performance, we apply exponential smoothing (ETS) and its inverse, named inverse-ETS, boosting the model’s predictive accuracy. We leverage air pollution datasets from various cities to verify the effectiveness of the proposed ADNNet. The results demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) models like LSTM, N-BEATS, Informer, Autoformer and VMD-TCN. The compressed model with reducing the mean absolute error (MAE) by 4.17%–16.12%, decreasing the root mean squared error (RMSE) by 3.52%–12.72%, and reducing the mean absolute scaled error (MASE) by 5.26%–17.28%. The source code of our model is available at https://github.com/xiankuiwu/AQI-Attention-based-DNN . • A deep neural network that integrates MLP and attention mechanism for AQI prediction. • Local features captured to boost model’s performance. • Incorporating a Bayesian optimization algorithm to tun network’s architecture.
Wu et al. (Sat,) studied this question.