基于长短时记忆网络(LSTM)的南水北调中线水位预测

Water Level Forecasting in Middle Route of the South-to-North Water Diversion Project(MRP)Based on Long Short-term Memory(LSTM)

长距离调水工程闸前水位受诸多水力控制因素影响,其波动趋势具有很强的非线性和随机性特征,难以用水动力机理模型高精度模拟,成为长距离输水调度方案制定的一大障碍。提出了一种基于深度学习网络的闸前水位预测新方法,建立了一个三层的LSTM水位预测模型,并应用于南水北调中线京石段的闸前水位预测,与深度神经网络(DNN)预测结果进行了对比。结果显示LSTM预测结果具有很高的精度,纳什系数高达0.99,均方根误差最高为0.029 m,能很好地预测水位波动趋势,预测效果比DNN更好。总结在LSTM模型构建时应考虑最大迭代次数对计算效率影响以及LSTM隐藏单元数目和学习率对精度的影响。本研究可为长距离调水工程水位预判、调度预警、水资源调度决策以及闸门智能控制提供重要参考。

The water level immediately upstream the gate of long-distance water diversion project is affected by many hydraulic control factors,and its fluctuation trend has strong non-linear and stochastic characteristics,making it difficult to simulate with high accuracy by hydrodynamic mechanism models,which remains a challenge to the diversion scheme.A new method of water level immediately upstream the gate prediction is proposed in this study based on deep learning network,and a three-layer LSTM water level prediction model is established and applied to water level immediately upstream the gate prediction which is compared with the deep neural network(DNN)in Jingshi section of MRP.The results show that the proposed model predict the trend of water level fluctuation better than DNN with high accuracy of Nash coefficient up to 0.99 and root mean square error up to 0.029 m.In conclusion,the influence of iterations on the calculation efficiency and the influence of the number of LSTM hidden units and the learning rate on accuracy should be considered in construction of LSTM model.Important reference can be provided for water level prediction,scheduling warning,water resource scheduling decision and intelligent gate control in long-distance water diversion project.