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 four bridges of the Han river: Cheongdam, Jamsu, Hangang, and Haengju. 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：汉江水位预测对于交通管控和自然灾害预防具有重要意义。与汉江相关的变量众多且相互关联复杂。本文提出了一种新型Transformer模型，该模型利用变量间基于先验知识的因果关系，对汉江四座桥梁（清潭大桥、蚕室大桥、汉江大桥、幸州大桥）的水位进行预测。通过将因果结构形式化为多层网络并采用掩码方法，本文提出的模型同时考虑了时空因果关系。该方法使得模型具备与先验知识一致的可解释性。在真实数据分析中，我们采用2016年至2021年的汉江数据集，将所提模型与深度学习模型进行了比较。