水库水位的VMD-CNN-GRU混合预测模型
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1.南京信息工程大学;2.上饶农业技术创新研究院;3.辽宁省葠窝水库管理局有限责任公司

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国家自然科学基金(62076136)


VMD-CNN-GRU hybrid prediction model of reservoir water level
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1.Nanjin University of Information Sincise &2.Technology;3.Shangrao Agricultural Technology Innovation Research Institute;4.Liaoning Shenwo Reservoir Authority Co., Ltd

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    摘要:

    水库水位预测为其运营、防洪、水资源调度管理提供了重要决策支持。准确可靠的预测对水资源的最优管理起着至关重要的作用。水动力学预测方法对输入边界条件要求较高,预测误差较大,难以满足水位实时调度的要求。针对水库水位数据的非线性、不稳定性以及复杂的时空特性,提出了一种融合了自适应变分模态分解(VMD)、卷积神经网络(CNN)和门控循环单元(GRU)的混合水库水位预测模型。其中,VMD通过对水位序列进行分解消除噪声,CNN用于有效提取水位数据的局部特征,GRU用于提取水位数据的深层时间特征。以葠窝水库日水位预测进行实例分析,与多个相关模型对比,分析结果表明,新模型在选取的评价指标上均表现最佳。其中,均方根误差为0.1500、平均绝对误差为0.1063、平均绝对百分比误差为0.1145,而纳什系数高达0.9953。特别地,本文选择的GRU与长短时记忆网络(LSTM)相比,运算效率提高显著。因此,新模型预测的高精度、高运算效率,更适合实际水库水位实时调度的需求。

    Abstract:

    The prediction of reservoir water level provides important decision support for its operation, flood control and water resources operation and management.Accurate and reliable prediction plays an important role in the optimal management of water resources. Hydrodynamic prediction method requires high input boundary conditions and large prediction error,which is difficult to meet the requirements of real-time water level regulation.Aiming at the nonlinearity, instability and complex temporal and spatial characteristics of reservoir water level data,a hybrid reservoir water level prediction model integrating adaptive Variational Modal Decomposition (VMD),Convolutional Neural Networks(CNN) and Gated Recurrent Unit (GRU) is proposed. Among them,VMD eliminates noise by decomposing the water level sequence,CNN is used to effectively extract the local features of water level data,and GRU is used to extract the deep time features of water level data.Taking the daily water level prediction of Shenwo reservoir as an example,compared with several related models,the analysis results show that the new model performs best in the selected evaluation indexes. Among them, the Root Mean Square Error is 0.1500, the Mean Absolute Error is 0.1063,the Mean Absolute Percentage Error is 0.1145,and the Nash–Sutcliffe efficiency coefficient is as high as 0.9953.In particular,the operation efficiency of GRU selected in this paper is significantly improved compared with Long and Short-term Memory network(LSTM). Therefore,the new model has high accuracy and high operation efficiency, and is more suitable for the real-time operation of reservoir water level.

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  • 收稿日期:2022-07-17
  • 最后修改日期:2022-10-07
  • 录用日期:2022-10-26
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