基于时序分解与机器学习的非平稳径流序列集成模型与应用

Integrated model and application of non- stationary runoff based on time series decomposition and machine learning

揭示变化环境下非平稳径流序列波动特征,可为提高径流预测精度和涉水工程规划提供支撑。针对径流序列具有非平稳性、周期性和异方差性的特征,收集长江流域攀枝花、城陵矶和大通站2008—2018年实测径流资料,基于周期趋势分解法(STL)将原始数据分解为周期项、趋势项和剩余项,结合各子序列特征采用多模型集成获取未来径流的综合预测值,并将预测结果与Prophet、LSTM和GARCH等单一模型进行对比。结果表明:联合机器学习和时序分解的集成模型在多个评价指标上均优于单一模型,且对异方差效应显著的站点模拟精度提升明显;验证期内3个站点的纳什效率系数分别为0.96、0.95和0.93,表明该模型能有效模拟长江流域径流波动过程。

Revealing the fluctuating characteristics of non-stationary runoff series under changing environments can improve the precision of runoff prediction and support water-related project planning.Given the characteristics of non-stationarity, periodicity, and heteroscedasticity of runoff series, the observed runoff data from 2008 to 2018 were collected from Panzhihua, Chenglingji, and Datong stations in the Yangtze River basin, and based on the seasonal-trend decomposition method, the original data was decomposed into periodic sequence, trend sequence, and residual sequence.Combined with the features of each subsequence, an integrated model was applied to obtain the total predicted value of future runoff, and the results were compared with the single model of Prophet, LSTM,and GARCH.The results show that the integrated model combined with time series decomposition and machine learning is superior to the single model in different evaluation indexes, and the simulation accuracy of stations with a strong heteroscedasticity effect is significantly improved.The Nash-Sutcliffe efficiency coefficient of the three stations in the validation period is 0.96,0.95,and 0.93,respectively, indicating that the model can effectively simulate the runoff fluctuation process in the Yangtze River basin.