基于时空特征挖掘的流量过程智能模拟方法

为减轻洪水灾害事件可能带来的严重后果，实现对流量的及时、准确预测，提出一种基于时空特征挖掘的流量过程智能模拟方法。该方法首先从空间角度入手，建立测站之间的拓扑结构关系；再利用图卷积网络进行空间挖掘；最后利用门控循环单元进行时序挖掘。试验结果表明，基于时空特征挖掘的流量过程智能模拟方法比基于单一特征的模拟方法效果更好。

Intelligent simulation method of runoff process based on spatiotemporal feature mining

To reduce the serious consequences of flood disaster events, the river discharge should be predicted timely and accurately to provide decision support for the flood forecasting. In this study, an intelligent simulation method of runoff process based on the spatiotemporal feature mining was proposed. Firstly, the topological diagram of hydrological stations was established from the spatial point of view. Then, this study proposed a novel framework that incorporated the graph convolutional networks (GCN) for mining the spatial feature. The gated recurrent unit was adopted to capture temporal features for the hydrological prediction. The experimental results show that the intelligent hydrological forecasting model based on the spatiotemporal feature mining is better than other models with single feature.