Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Abstract

Forecasting the water level of the Han river is essential 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 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.