基于G-LSTM模型的短期径流预报——以长江上游寸滩断面-三峡入库断面为例

Short-term runoff forecast based on G-LSTM model:case of Cuntan section to section at Three Gorges Reservoir in upper reaches of Yangtze River

长短时记忆神经网络(LSTM)具有极强的复杂多变量时间序列非线性关系拟合和历史数据认知能力,适用于对径流这类复杂时间序列过程进行模拟和预报。基于LSTM,采用灰色关联分析法(GRA),选取合适的预报因子建立G-LSTM预报模型,探究了该模型在短期径流预报中的应用和效果。将该方法应用于长江上游寸滩断面-三峡入库断面的径流模拟,并与传统的新安江模型、BP神经网络、LSTM模型的模拟结果进行比较。结果表明:与传统学习的近似映射相比,G-LSTM模型具有优秀的非线性函数学习能力,率定期与检验期的确定性系数均在0.9以上,明显优于其他两种模型的模拟结果。G-LSTM模型显著提高了短期径流预报精度,是一种有效的径流预测方法。

The physical mechanism and process of catchment generation and drainage in the river basin are extremely complicated,especially the variation of boundary conditions,which makes the construction of traditional hydrological models very difficult.And increasing the complexity of hydrological models does not necessarily bring about a significant improvement in forecast accuracy.The long short-term memory(LSTM)has a strong ability to fit non-linear relationships in complex multivariable time series and the ability to recognize historical data,which is suitable for simulating and forecasting complex time series processes such as runoff.In this study,the grey correlation analysis method was used to select appropriate forecasting factors,combined with LSTM,a G-LSTM forecasting model was established,and its application effect in short-term runoff forecasting is explored.The method was applied to the Cuntan section-Three Gorges Reservoir.By comparing the result of the this model with that of Xinanjiang model,BP neural network and LSTM model,the results showed that G-LSTM had an excellent ability to learn non-linear functions compared with the approximate mapping of traditional learning.Deterministic coefficients of the periodic and inspection periods were all above 0.9,which was significantly better than the simulation results of the other two models.G-LSTM can significantly improve short-term runoff forecast accuracy and is an effective method for runoff prediction.