Forecasting the water level of the Han river is important to control traffic and avoid natural disasters. There are many variables related to the Han river and they are intricately connected. In this work, we propose a novel transformer that exploits the causal relationship based on the prior knowledge among the variables and forecasts the water level at the Jamsu bridge in the Han river. Our proposed model considers both spatial and temporal causation by formalizing the causal structure as a multilayer network and using masking methods. Due to this approach, we can have interpretability that consistent with prior knowledge. In real data analysis, we use the Han river dataset from 2016 to 2021 and compare the proposed model with deep learning models.

翻译：预测汉江水位对交通管控和自然灾害防治具有重要意义。与汉江相关的变量众多且相互关联复杂。本文提出一种新型Transformer模型，该模型基于变量间的先验知识挖掘因果关系，用于预测汉江盘浦大桥水位。我们通过将因果结构形式化为多层网络并采用掩码方法，使模型能够同时考虑空间与时间因果关系。这一方法使模型具备与先验知识一致的因果可解释性。在真实数据分析中，我们采用2016至2021年的汉江数据集，将所提模型与深度学习模型进行了对比。