Air Quality Forecasting Using the GRU Model Based on Multiple Sensors Nodes

This letter presents an air quality forecasting method whose main strength is that the prediction accuracy and reliability can be improved effectively based on observed data of multiple sensor nodes. In our solution, for a certain sensor node, several spatial correlated neighboring sensor nodes are first selected according to the mutual information among nodes. Then, time-series data of these nodes are concatenated and fed into a gated recurrent unit (GRU) network to train the model for air quality forecasting. The proposed method is evaluated on the Intel Lab dataset and achieves better performance with about 14% and 5% reductions in terms of mean absolute error (MAE) and root mean square error (RMSE) compared to single node-based forecasting. Besides, the feasibility of the proposed method has been validated through a practical application of indoor air quality monitoring.