Probabilistic air quality forecasting using deep learning spatial–temporal neural network

Regional air quality monitoring, a critical component of sustainable development is realized through various air quality observation stations established across a region. Accurate forecasting of air quality data collected from these observation stations requires the modelling of spatial–temporal patterns in the data. Deep learning algorithms, known for their ability to capture layers of abstraction, can proficiently achieve spatial–temporal modeling. However, deterministic models that produces point forecast does not consider the underlying model uncertainty during prediction and are therefore less reliable for real-time applications. Probabilistic forecasting models that forecast prediction intervals rather than point estimates can overcome this through uncertainty quantification. The objective of the proposed study is three-fold: i) develop an efficient deterministic deep learning spatial–temporal neural network named DL-STNN for spatial–temporal air quality forecasting; ii) investigate different approaches to uncertainty quantification in deep learning models and integrate some of them, such as Monte-Carlo Dropout, Ensemble Averaging, Gaussian Process Regression, Quantile Regression, and Bayesian Inference, in tandem with DL-STNN to facilitate probabilistic forecasting; and iii) evaluate the developed deterministic and probabilistic models, using a real-world Delhi air quality dataset. The evaluation results show that, among the deterministic models, DL-STNN outperforms the baselines with 39.8% more accurate predictions and performs consistently across all seasons in Delhi. Furthermore, among the DL-STNN-based tandem models that performed probabilistic forecasting, Bayesian DL-STNN proved efficient. It does 13% more accurate point forecasting and has 20% higher suitability score than the other tandem models, indicating that Bayesian inference adapts DL-STNN more reliable for real-time applications.