  Heterogeneous data are commonly adopted as the inputs for some models that
predict the future trends of some observations. Existing predictive models
typically ignore the inconsistencies and imperfections in heterogeneous data
while also failing to consider the (1) spatial correlations among monitoring
points or (2) predictions for the entire study area. To address the above
problems, this paper proposes a deep learning method for fusing heterogeneous
data collected from multiple monitoring points using graph convolutional
networks (GCNs) to predict the future trends of some observations and evaluates
its effectiveness by applying it in an air quality predictions scenario. The
essential idea behind the proposed method is to (1) fuse the collected
heterogeneous data based on the locations of the monitoring points with regard
to their spatial correlations and (2) perform prediction based on global
information rather than local information. In the proposed method, first, we
assemble a fusion matrix using the proposed RBF-based fusion approach; second,
based on the fused data, we construct spatially and temporally correlated data
as inputs for the predictive model; finally, we employ the spatiotemporal graph
convolutional network (STGCN) to predict the future trends of some
observations. In the application scenario of air quality prediction, it is
observed that (1) the fused data derived from the RBF-based fusion approach
achieve satisfactory consistency; (2) the performances of the prediction models
based on fused data are better than those based on raw data; and (3) the STGCN
model achieves the best performance when compared to those of all baseline
models. The proposed method is applicable for similar scenarios where
continuous heterogeneous data are collected from multiple monitoring points
scattered across a study area.
