基于SSA-LSTM模型的日水位预测——以涡河流域涡阳闸为例

Daily Water Level Prediction Based on SSA-LSTM Model——Case Study of Guoyang Sluice in Guohe River Basin

水位的准确预测可以指导城市的防洪减灾举措及水利工程建设,提升城市洪涝灾害应急响应速度.基于数据驱动的水位预测模型,尤其是LSTM模型,在模拟自然界中水文要素的强非线性关系时展现出优势从而得到广泛应用.然而,自然界中水文数据的采集往往伴随着噪声以及人为干扰因素,这些问题影响了模型的预测性能.针对这一问题,本文开发了一种新的组合模型,即SSA-LSTM模型.该模型首先利用SSA方法将观测到的时间序列分解为周期、趋势和噪声分量,接着利用LSTM对SSA方法去噪后的序列进行模型训练并得到最终预测结果.本文选取涡河流域涡阳闸1971年5月至2020年12月的闸上水位为数据集,1)利用奇异谱分析方法将原始水位时序数据分解为多个趋势和噪声分量(RC_(1)–RC_(12)),选取分量(RC_(1)–RC_(10))为趋势项并重构为新的水位时序信号;2)利用LSTM模型对重构的信号进行了训练和验证,并将预测结果与LSTM模型的结果进行了对比;3)为得到最优的SSA-LSTM模型,针对不同的时间步长(7、14、21、28、35天)开展了单步预测性能评估实验,实验结果表明,在不同的时间步长下,SSA-LSTM水位预测模型的决定系数R^(2)、均方根误差RMSE、平均绝对误差百分比MAPE均优于LSTM模型.由此可见,采用SSA方法对涡阳闸水位的预处理可有效提高LSTM的预测效果,相比于传统LSTM模型,SSA-LSTM模型具有高可靠和低误差的特点,在水位预测应用中更具适应性,可以为城市防洪、灌溉、供水等水利措施的合理调度提供更优的决策依据.

Accurate prediction of the water level can guide urban flood control and calamity reduction,as well as water conservancy construction to improve the speed of urban flood emergency response.Data-driven water level prediction models,especially the long short-term memory(LSTM)models,have shown advantages in simulating the strong nonlinear relationships of hydrological elements in nature and thus are widely used.However,the collection of hydrological data in nature is often accompanied by noise and human interference factors,which affect the prediction performance of the models.To address this problem,this study develops a new prediction model combining singular spectrum analysis(SSA)and LSTM,i.e.,the SSA-LSTM model.Specifically,SSA first decomposes the observed time series into periodic,trend,and noise components,and then LSTM is used to train the model on the denoised time series to obtain the final prediction results.In this study,the water levels of Guoyang Sluice in the Guohe River Basin from May 1971 to December 2020 are selected as the data set for experiments:1)The original time series data of water levels are decomposed into multiple trend and noise components(RC_(1)–RC_(12))by SSA,and the components(RC_(1)–RC_(10))are selected as the trend term and reconstructed into a new water-level time-series signal.2)The reconstructed signal is trained and verified by the LSTM model,and the predicted results are compared with those of the LSTM model.3)To obtain the optimal SSA-LSTM model,this study conducts single-step prediction performance evaluation experiments for different time steps(7,14,21,28,and 35 d).The experimental results reveal that the coefficient of determination R^(2),root mean square error(RMSE),and mean absolute percentage error(MAPE)of the SSA-LSTM water-level prediction model are better than those of the LSTM model at different time steps.The pre-processing of the water level at the Guoyang Sluice by SSA can effectively improve the prediction effect of LSTM.Compared with the traditional LSTM models,the SSALSTM model has the characteristic of high reliability and low errors and is more adaptable in water-level prediction applications,which can provide a better decision basis for the rational scheduling of urban flood control,irrigation,water supply,and other water conservation measures.