| 摘要: |
| 洞庭湖是长江中游的重要湖泊，准确模拟其各输入、输出站点的径流响应关系，对湖区防汛抗旱和生态保护至关重要。针对复杂水力连接下洞庭湖流域多站点径流过程的时空非线性关联特性，本研究提出了一种基于图神经网络的多输入—多输出径流响应模型，首先利用长江、洞庭湖和四水的流域拓扑空间结构，将各站点的原始观测序列转化为图结构数据，以表征多站点之间的空间关联特性；然后，通过互相关分析法研究各站点观测变量之间的时滞关系，确定模型的输入特征步长；最后，利用图神经网络对数据中的站点特征进行聚合与更新，以捕捉关键控制站点间的复杂时空依赖性，提高多站点径流模拟的准确性和可靠性。结果表明：在洪水事件中，图神经网络（GNN）的纳什效率系数NSE和平均绝对误差MAE相比前馈神经网络（BP）和长短期记忆神经网络（LSTM）模型均提高5%以上，且相关性系数R^2均超过0.97，在枯水断流事件中，召回率TPR和精度Precision普遍超过0.96。GNN在洪水和枯水断流等水文事件模拟方面具有明显优势，可为洞庭湖防汛抗旱和生态治理提供科学依据。 |
| 关键词: 图神经网络 径流响应模型 多输入—多输出 时空关联特征 |
| DOI： |
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| 基金项目:国家重点研发计划(2021YFC3200303)资助。 |
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| Exploring graph neural networks for accurately simulating flood and drought events of the Donting lake |
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Wei Yilong, Zhou Yanlai, Luo Yuxuan
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State Key Laboratory of Water Resources Engineering and Management,Wuhan University
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| Abstract: |
| Dongting Lake is an important lake in the middle reach of the Yangtze River, and accurately modelling the runoff corresponding relationships of its various input and output stations is crucial for regional ecological protection and flood control and drought defense. To address the complex runoff response relationships in the Dongting Lake basin, this study proposes a multiple-input and multiple-output runoff response model based on graph neural networks. Firstly, the model utilizes the basin topological spatial structure of the Yangtze River, Dongting Lake and Sishui to transform the original observation sequences at each station into graph-structured data to characterize the spatial characteristics of the basin. Secondly, through the mutual correlation analysis method, the time lag relationship between the observed variables at each station is identified to determine the input feature step of the model. Finally, graph neural networks are employed to aggregate and update the features to capture the complex spatial and temporal dependencies among the control station, and to realize the runoff simulation at multiple stations. The results show that in the flood event, compared with the backpropagation neural network (BP) and the long-short term memory neural network (LSTM) models, the graph neural network (GNN) model can achieve the improvement rates over 5% for Nash-Sutcliffe efficiency coefficient (NSE) and mean absolute error (MAE) indicators, and the correlation coefficients (R^2) is more than 0.97, while in the dry water cutoff events, the True Positive Rate (TPR) and Precision are generally more than 0.96. GNN has significant advantages in the simulation of hydrological events such as floods and droughts, which can provide a scientific support for the ecological protection of Dongting Lake and its flood control and drought resistance. |
| Key words: Graph neural network (GNN) Runoff response modelling Multiple-input and multiple-output Spatio-temporal correlation |
