基于多重演化模式重构的水文预报模型及应用

Application of hydrological forecast model based on multiple evolutionary reconstruction

因长期受人类活动、气候变化等多重因素作用,水文时间序列表现出多时间尺度、多频率、动态变化、自记忆性等复杂性特征,增加了水文预报结果的不确定性。本文将经验模态分解模型,核主成分分析模型和支持向量机模型耦合,建立了针对复杂性水文时间序列的预报模型,并采用NASH效率系数、自相关系数、相对误差作为模拟预测精度及参数率定的多目标判断标准。模型应用于黄河花园口水文站径流序列的长期水文预报中,结果表明:模型预报时段长,具有较好的预测准确性和实践应用价值。该模型为多重因素作用的复杂性水文时间序列预报提供了一种方法。

Due to the long-term effects of human activities and climate change,the hydrological time series exhibit more complicated variation features such as multiple time scales,many ruense dynamic change and self memory function,which increase the uncertainty of hydrological forecast result. The pape established a new hydrologic forecast model based on the empirical mode decomposition model,kernel principal component analysis model and support vector machine model,and choose Nash efficiency,self correlation coefficient,relative error as the multi objective criteria of forecasting precision and parameter calibration. The model was applied to the long-term runoff series at Huayuankou hydrology station of the Yellow River. The results show that the forecast time of the model is long and has better accuracy and practical value. The model can provide a method for the prediction of complex hydrological time series of multiple factors effect.