基于时间卷积网络的长江下荆江航道水位预测

Water level prediction of lower Jingjiang Waterway in Yangtze River based on temporal convolution network

航道水位的精准预测对保障船舶通航安全具有重要意义。以长江下荆江航道为研究区域,采用2019—2020年水文时间序列数据为训练集,2021年数据为验证集,构建基于时间卷积网络(TCN)的长江下荆江水位变化预测模型,并与基于长短时记忆神经网络(LSTM)和支持向量机(SVM)的水位预测模型进行计算精度的对比。结果表明:不同站点TCN对应的最优输入时间窗口存在一定差异,监利站、调弦口站及石首站的最优输入时间窗口分别为前2 d、前2 d和前3 d;TCN在2021年下荆江各站点水位预测结果的纳什系数(ENS)和决定系数(R^(2))均高于0.995,均方根误差(ERMS)小于0.21 m,整体预测效果优于LSTM,两者预测精度均较高,均显著优于SVM;但随着预测时间尺度的增加,水位预测精度整体呈下降趋势;TCN模型各站点枯水期大部分时段的水位预测绝对误差小于0.2 m,这表明TCN在航道水位预测领域具有较好的应用潜力。通过分析TCN在航道水位预测中的适用性和优越性,可为长江航道水位预测精度提升和船舶安全通航提供技术支撑。

Accurate prediction of water level in waterway is of great significance for ensuring ships’navigational safety.The lower Jingjiang waterway in the Yangtze River was taken as the research area,and the hydrological data from 2019 to 2020 and from 2021 were adopted as the train set and test set,respectively.A temporal convolution network(TCN)model was developed for water level prediction of the Lower Jingjiang waterway.Then the long shortterm memory network(LSTM)and the support vector machine(SVM)were constructed for accuracy comparing with TCN.The results showed that there were differences of optimal input time windows of TCN in different stations.Jianli station,Tiaoxiankou station and Shishou station’s optimal input time windows were 2 days,2 days and 3 days,respectively.In 2021,the Nash-Sutcliffe efficiency coefficient and determination coefficient of the water level prediction of TCN at each station in the lower Jingjiang River were higher than 0.995,and the RMSE was basically below 0.21 m.The overall performance of TCN was better than LSTM,and both of them could accurately predict the water level process and perform better than SVM.However,with the increase of prediction time scale,the prediction accuracy of water level showed a downward trend.In terms of different periods,the absolute error of TCN water level prediction in dry season was basically below 0.2 m,indicating that TCN has a great potential in the field of water level prediction.By analyzing the applicability and superiority of the TCN model,this study can provide technical support for improving the accuracy of water level prediction in the Yangtze River channel and the safe navigation of ships.