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为进一步提升水文报汛数据质量管理体系，实现水文报汛数据实时异常监测，拟结合深度学习算法在水文专业的交叉应用，通过分析水文报汛数据的时序特征，结合了卷积神经网络(CNN)和长短期记忆神经网络(LSTM)来增强对特征的处理，同时考虑河道水量传播过程，融合上下游水位、流量数据中隐含的空间关联特征，以实现多步时序预测，挑选长江流域上中下游典型监测站3年的数据构建数据集并进行模拟，结果表明，构建的CNN-LSTM时序预测模型在水位决定系数(R2)达到0.77，流量模拟的决定系数(R)达到0.84，能较好拟合水文预测结果；在此基础上构建了“预测-残差-判别”一体化的水文数据异常检测框架，在三峡水库的水位模拟中异常值检出率为100%，决定系数(R)达到0.991，有效实现报汛异常值检测，能为提高流域水文报汛数据质量提供智能算法基础。
Abstract：To further enhance the quality management system of hydrological flood reporting data and achieve real-time anomaly monitoring of hydrological flood reporting data， it is proposed to combine the cross-application of deep learning algorithms in the hydrological field. By analyzing the time series characteristics of hydrological flood reporting data and integrating convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) to enhance feature processing， while considering the water flow process in rivers， the spatial correlation features hidden in the water level and flow data of upstream and downstream are fused for the first time to achieve multi-step time series prediction. A dataset was constructed using three years of data from typical monitoring stations in the upper， middle and lower reaches of the Yangtze River for simulation. The results show that the constructed CNN-LSTM time series prediction model has a water level determination coefficient (R) of 0.77 and a flow simulation determination coefficient (R) of 0.84， which can well fit the hydrological prediction results. On this basis， an integrated hydrological data anomaly detection framework of "prediction-residual-discrimination" was constructed. In the water level simulation of the Three Gorges Reservoir， the anomaly detection rate was 100%， and the determination coefficient (R) reached 0.991， effectively achieving the detection of abnormal values in flood reporting， which can provide an intelligent algorithm basis for improving the quality of hydrological flood reporting data in the basin.
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基本信息:
中图分类号:TP18;P338
引用信息:
[1]秦洪亮,秦昊,纪国良.基于深度学习的时序预测模型在水文报汛数据异常检测中的应用研究[J].水利水电技术(中英文)().
基金信息:
中国长江三峡集团有限公司科研项目资助（0704218）; 国家重点研发计划(2023YFC3209105); 国家自然科学基金资助项目（U2340211）
2025-05-14
2025-05-14
2025-05-14