Forecasting the water level of the Han river is important to control the 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 the interpretability that consistent with prior knowledge. Additionally, we propose a novel recalibration method and loss function for high accuracy of extreme risk in time series. 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：预测汉江水位对于交通管控和防灾减灾具有重要意义。汉江涉及众多变量，且彼此关联复杂。本文提出一种新型Transformer模型，该模型基于变量间先验知识利用因果关系，实现对汉江四座桥梁（清潭、蚕室、汉江、幸州）的水位预测。所提模型通过将因果结构形式化为多层网络并采用掩码方法，同时考虑空间与时间因果性。基于该方法，模型能够提供与先验知识一致的阐释性。此外，为提升极端风险时序预测精度，我们创新性地提出重新校准方法与损失函数。在真实数据分析中，采用2016至2021年汉江数据集，将所提模型与深度学习模型进行对比。