【摘要】 为了提高流域径流量预报的准确率，考虑数据驱动水文模型缺乏模型透明度与物理可解释性的问题，提出了一种使用图注意力网络与基于长短期记忆网络（LSTM）的双阶注意力机制（GAT-DALSTM）模型来进行径流预报。首先，以流域站点的水文资料为基础，引入图神经网络提取流域站点的拓扑结构并生成特征向量；其次，针对水文时间序列数据的特点，建立了基于双阶注意力机制的径流预报模型对流域径流量进行预测，并通过基于注意力系数热点图的模型评估方法验证所提模型的可靠性与透明度。在屯溪流域数据集上，将所提模型与图卷积神经网络（GCN）和长短期记忆网络（LSTM）在各个预测步长下进行比较，实验结果表明，所提模型的纳什效率系数分别平均提高了3.7%和4.9%，验证了GAT-DALSTM径流预报模型的准确性。从水文与应用角度对注意力系数热点图进行分析，验证了模型的可靠性与实用性。所提模型能为提高流域径流量的预测精度与模型透明度提供技术支撑。更多还原

【Abstract】 To improve the accuracy of watershed runoff volume prediction,and considering the lack of model transparency and physical interpretability of data-driven hydrological model,a new runoff forecast model named Graph Attention neTwork and Dual-stage Attention mechanism-based Long Short-Term Memory network(GAT-DALSTM) was proposed. Firstly,based on the hydrological data of watershed stations,graph neural network was introduced to extract the topology of watershed stations and generate the feature vectors. Secondly,according to the characteristics of hydrological time series data,a runoff forecast model based on dual-stage attention mechanism was established to predict the watershed runoff volume,and the reliability and transparency of the proposed model were verified by the model evaluation method based on attention coefficient heat map. On the Tunxi watershed dataset,the proposed model was compared with Graph Convolution Neural network(GCN)and Long Short-Term Memory network(LSTM)under each prediction step. Experimental results show that,the Nash-Sutcliffe efficiency coefficient of the proposed model is increased by 3. 7% and 4. 9% on average respectively,which verifies the accuracy of GAT-DALSTM runoff forecast model. By analyzing the heat map of attention coefficient from the perspectives of hydrology and application,the reliability and practicability of the proposed model were verified. The proposed model can provide technical support for improving the prediction accuracy and model transparency of watershed runoff volume.更多还原