Time-series modeling has shown great promise in recent studies using the latest deep learning algorithms such as LSTM (Long Short-Term Memory). These studies primarily focused on watershed-scale rainfall-runoff modeling or streamflow forecasting, but the majority of them only considered a single watershed as a unit. Although this simplification is very effective, it does not take into account spatial information, which could result in significant errors in large watersheds. Several studies investigated the use of GNN (Graph Neural Networks) for data integration by decomposing a large watershed into multiple sub-watersheds, but each sub-watershed is still treated as a whole, and the geoinformation contained within the watershed is not fully utilized. In this paper, we propose the GNRRM (Graph Neural Rainfall-Runoff Model), a novel deep learning model that makes full use of spatial information from high-resolution precipitation data, including flow direction and geographic information. When compared to baseline models, GNRRM has less over-fitting and significantly improves model performance. Our findings support the importance of hydrological data in deep learning-based rainfall-runoff modeling, and we encourage researchers to include more domain knowledge in their models.
翻译:最近利用LSTM(长期短期内存)等最新深层学习算法进行的研究显示,时间序列模型模型的模型模型显示,最近利用LSTM(长期短期内存)等最新深层算法进行的研究显示,这些研究大有希望,这些研究主要侧重于流域规模降雨模型或流流流预测,但大多数只考虑一个单一的流域单位。虽然这种简化非常有效,但没有考虑到空间信息,这可能导致大型流域出现重大错误。一些研究调查了GNN(大地神经网络)数据集成数据集成的情况,将一个大型流域分解成多个次流域,但每个次流域仍作为一个整体处理,流域内的地理信息没有得到充分利用。在本文件中,我们提议GNRRRM(Graph Neural Rainf-Runoff模型)是一个新型的深层次学习模型,充分利用高分辨率降水量数据的空间信息,包括流动方向和地理信息。与基线模型相比,GNRRRMM没有太多的过大和显著改进模型的性能。我们的调查结果支持水文数据在深度降雨流流模型中的重要性,我们鼓励研究人员将更多的领域知识纳入模型。