Accurate prediction of runoff is critical concerning reservoir management and disaster preparedness. Data-driven methods are progressively applied to runoff prediction tasks and have led to impressive results. However, existing data-driven methods are hardly considered to the runoff generation process and the spatial characteristics of basins in the models due to the lack of a priori knowledge guidance. Here a structured approach is provided to develop the perceptual model for runoff generation and model the behavior in groups at different locations and scales; considering the hierarchical structure of basin systems, a short-term runoff forecasting model with spatial perception and scale interaction, i.e., the hierarchical attention network, is developed based on the encoder-decoder structure and attention mechanism. Compared to the single- and multi-step prediction performance of the six baseline models, the NSE improved by an average of 2.41, 9.68, and 12.14%, respectively. This implies that incorporating basin-related knowledge in modeling and considering runoff generation processes and spatial connectivity can improve prediction accuracy, and the necessity of considering conceptual mechanisms in data-driven models.

中文翻译：

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用于短期径流预测的分层注意力网络


径流的准确预测对于水库管理和防灾至关重要。数据驱动的方法逐渐应用于径流预测任务，并取得了令人印象深刻的结果。然而，现有的数据驱动方法由于缺乏先验知识指导，在模型中几乎没有考虑径流生成过程和流域的空间特征。这里提供了一种结构化方法来开发径流生成的感知模型，并对不同位置和规模的群体行为进行建模；考虑流域系统的层次结构，基于编码器-解码器结构和注意机制，建立了具有空间感知和尺度交互的短期径流预测模型，即层次注意网络。与六个基线模型的单步和多步预测性能相比，NSE 平均分别提高了 2.41%、9.68% 和 12.14%。这意味着在建模中纳入流域相关知识并考虑径流生成过程和空间连通性可以提高预测精度，并且有必要在数据驱动模型中考虑概念机制。