A Domain-Specific Bayesian Deep-Learning Approach for Air Pollution Forecast
Yang Han, Jacqueline C. K. Lam, Victor O. K. Li, Qi Zhang
Abstract
Predicting air pollution concentration is crucial and beneficial for public health. This study proposes a domain-specific Bayesian deep-learning model for long-term air pollution forecast in China and the United Kingdom. Our proposed model carries three novelties: First, a domain-specific knowledge is integrated to take into account the strong statistical relationship between PM <inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> and PM <inline-formula><tex-math notation="LaTeX">$_{10}$</tex-math></inline-formula> as a regularization term; Second, an attention layer is included to capture the influential historical feature and the recursive temporal correlation of air quality data; Third, results generated from different multi-step forecast strategies are combined based on corresponding uncertainty measures to improve our model’s performance. Our model outperforms other baseline models. Results show that incorporating Bayesian and domain-specific knowledge into the deep learning model can reduce the prediction errors by a maximum of 3.7% and 12.4%, for Beijing and London, respectively. Specifically, incorporating domain-specific knowledge into the Bayesian deep-learning model reduces prediction errors whilst the integration of Bayesian techniques allows the fusion of different forecast strategies to improve prediction accuracy. In future, additional influential domain-specific features can be added to further improve our deep-learning model’s prediction accuracy and interpretability.