大型水库重要站点的水位预测是水库防洪中的重要问题,目前主要采用水动力学方法计算。但是由于该方法对输入边界条件的准确性要求较高,而实时调度情况下又很难完全满足此条件,因此容易造成较大的水位计算误差。对此,提出了循环神经网络模型,从水库运行的历史数据中挖掘知识,学习入库流量和坝前水位到目标站点水位的映射关系,不需要使用地形资料等数据以避免系统性误差的影响,既可以降低模型对输入边界准确性的要求,又可以在一定程度上提升水位预测精度。以三峡水库长寿站水位计算为例,使用2009—2019年数据训练、验证和测试模型,试验结果显示长寿站水位在接近三峡水库土地征用线时,计算误差在±0.4 m以内,精度优于水动力学模型,可满足实时调度对水位精度的要求。

The prediction of water level for important stations of large reservoirs is an essential issue in flood control. At present, hydrodynamics method is mainly used to calculate the water level. However, due to the high accuracy requirements of input boundary conditions, which is difficult to meet under real-time dispatching, the calculation error is likely to be large. In view of this, we present a recurrent neural network model to mine the knowledge from historical data of reservoir operation and to learn the mapping relationship between inlet flow (including main stream and tributaries) and the water level from in front of the dam to the target station. This method does not use terrain data to avoid the effect of systematic error, thereby reducing the requirement for input boundary accuracy on one hand and improve prediction accuracy on the other. In the experimental part, we compute the water level of Changshou Station of Three Gorges Reservoir. The data sets (including training, validation and test sets) consist of the operation data from 2009 to 2019. When the water level is close to the land requisition line, the prediction error is within±0.4 m, which is better than the result of water dynamic model. Therefore, the proposed method can meet the accuracy requirement of the real-time scheduling of water level.