  Forecasting the water level of the Han River is essential to control traffic
and avoid natural disasters. The stream flow of the Han River is affected by
various and intricately connected factors. Thus, a simple forecasting machine
frequently fails to capture its serial pattern. On the other hand, a complex
predictive model loses the interpretability of the model output. This work
proposes a neural network model with a novel transformer exploiting a causal
relationship based on prior knowledge. The transformer consists of
spatiotemporal attention weight that describes the spatial and temporal
causation with multilayer networks with masking. Our model has two
distinguished advantages against the existing spatiotemporal forecasting
models. First, the model allows the heterogeneous predictors for each site such
that a flexible regression is applicable to the causal network. Next, the model
is adapted to partially identified causal structures. As a result, we have
relaxed the constraints of the applicable causal network through our model. In
real data analysis, we use the Han River dataset from 2016 to 2021, compare the
proposed model with deep learning models, and confirm that our model provides
an interpretable and consistent model with prior knowledge, such as a
seasonality arising from the tidal force. Furthermore, in prediction
performance, our model is better than or competitive with the state-of-the-art
models.
