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:摘要:对汉江水位进行预测旨在控制交通和避免自然灾害。汉江诸多相关变量之间密切关联,因此我们提出了一种新型的transformer模型,它基于变量之间的因果关系进行建模,并预测汉江济州桥的水位。我们提出的模型同时考虑空间和时间上的因果关系,通过多层网络和掩码方法对因果结构进行形式化建模。由于此方法,我们可以得到一致于先前知识的可解释性。在实际数据分析中,我们使用了2016至2021年的汉江数据集,并将提出的模型与深度学习模型进行比较。