Short-term prediction of airway congestion index using machine learning methods

Abstract

This study presents a hybrid spatiotemporal convolutional long short-term memory network with tri-directional temporal features (SCLN-TTF) for airway congestion index prediction. The spatial features, temporal features and spatiotemporal features are taken as input variables, among which the temporal and spatiotemporal input datasets are categorized into forward time series datasets, backward time series datasets and datasets at the target time. The spatial features are extracted using the fast-approximate convolutions on graphs, and the temporal features are extracted using the long short-term memory network. The subsequent congestion index is the output variable. The model parameters are sequentially updated based on the recent collected data and the new predicting results. It is found that, in general, the proposed SCLN-TTF method achieves the most stable and satisfactory performance for all the selected airways. The models exhibit the best performance for the 60 min prediction time horizon in terms of MAPE, while the proposed SCLN-TTF shows superiority compared to the other benchmark methods particularly for the 10 min prediction. Then the graph convolutional network and extreme gradient boosting method are combined for high congestion index identification. The finding further confirms the superiority of the proposed method by considering both the spatial and temporal dependencies and integrating forward and backward features.