Building a Generalized Pre-Training Model to Predict River Water-Level from Radar Rainfall

In our previous work, we proposed a river water-level prediction method using deep learning, incorporating radar rainfall data in place of water-level and rainfall stations upstream of the prediction point. By introducing a newly defined flow distance matrix, transfer learning becomes available, i.e., even when data at the prediction point is scarce, accurate water-level predictions are made using inundation data from other rivers. However, this approach requires pre-selecting rivers that behave similarly to the prediction point for training, making it laborious to build prediction models for multiple rivers. Furthermore, the previous study only performed predictions for a single river, raising uncertainty about whether the method is applicable to water-level prediction for other rivers with different conditions. In this paper, we construct a generalized river water-level prediction model commonly applicable to multiple Japanese rivers by using inundation data from all Japanese Class-A rivers (the major river systems managed by the government) for pre-training, rather than only the rivers similar to the prediction site. Through evaluation, we showed that pre-training using all Class-A rivers yields higher prediction accuracy than pre-training using similar rivers across multiple rivers with varying conditions. This demonstrates that using all Class-A rivers for pre-training enables the construction of a generalized river water-level prediction model applicable to a wide range of rivers.