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：预测汉河水位对于控制交通和避免自然灾害非常重要。 有许多与汉河相关的变量,它们有着千丝万缕的联系。 在这项工作中,我们提出一个新的变压器,根据先前对变量的了解来利用因果关系,并预测汉河四座桥梁: Cheongdam、 Jamsu、Hangang和Haengju。 我们提议的模型通过将因果结构正规化为多层网络并使用掩码方法来考虑空间和时间因果关系。 由于这种方法,我们可以有与先前知识一致的可解释性。 此外,我们提出了一个新的重新校正方法和损失功能,以在时间序列中高度精确地反映极端风险。在实际数据分析中,我们使用汉河数据集,从2016年到2021年,并将拟议模型与深层学习模型进行比较。</s>