基于图卷积GRU网络的短时小时径流预报
Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
摘要：
由于气候变化，洪水的频率和影响预计将增加。预测径流量以及洪水，以便准备和减轻其在财产损失和人员伤亡方面的后果，这一点至关重要。本文提出了一种基于图卷积GRUs的模型来预测未来36小时的径流传感器位置使用上游河流网络。实验结果表明，在短期径流预测中，本研究中提出的模型提供了更好的性能比持久性基线和卷积双向GRU网络为选定的研究区域。
英文摘要：
The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.
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