摘要
针对渭河流域月径流序列的非平稳性日益加剧而难以对其进行精准预测的问题,提出了一种基于最优变分模态分解(OVMD)、随机配置网络(SCN)和递归多步预测策略的月径流序列多步预测模型。首先,利用OVMD将径流数据投影到不同频率的子序列中;然后通过SCN对每个分解部分进行预测,叠加得到单步预测结果;最后通过递归多步预测方法对未来较长时间的径流数据进行预测,得到多步预测结果。选取渭河流域华县水文站和咸阳水文站1970~2019年的实测月径流时间序列进行实例分析,并与其他常用模型进行对比,选取均方根误差RMSE、平均绝对误差MAE、平均绝对百分比误差MAPE以及纳什效率系数NSE对预测结果进行评价。研究结果表明:在华县水文站和咸阳水文站的单步预测试验中,OVMD-SCN模型的NSE分别达98.15%和98.52%,显著高于其他流行模型;在两个水文站的多步预测试验中,OVMD-SCN的各项评价指标均优于其他流行模型,表明所提方法可以精准预测5个月后的径流量。研究成果可为渭河流域的月径流精准预测提供技术支持。
The non-stationarity of monthly runoff series in Weihe River Basin is increasing and it is difficult to predict accurately,a multi-step prediction model of monthly runoff series based on optimal variational mode decomposition(OVMD),stochastic configuration networks(SCN)and recursive multi-step prediction strategy was proposed.Firstly,OVMD was used to project the runoff data into subsequences with different frequencies.Then,SCN was used to predict each decomposition part,and the single-step prediction results were obtained by superposition.Finally,the recursive multi-step prediction method was used to predict the runoff data for a long time in the future,and the multi-step prediction results were obtained.The measured monthly runoff time series of Huaxian Hydrological Station and Xianyang Hydrological Station from 1970 to 2019 were selected for case analysis,and we compared the predicted results with other popular models.RMSE,MAE,MAPE and NSE were selected to evaluate the prediction results.The results showed that the NSE of the OVMD-SCN model in the single-step prediction experiments of Huaxian Hydrological Station and Xianyang Hydrological Station reached 98.15%and 98.52%,respectively,which were significantly higher than other popular models.In the multi-step prediction experiments of two hydrological stations,the evaluation indexes of OVMD-SCN were better than other popular models.It showed that the proposed method can accurately predict the runoff in the future 5 months.The research results can provide a new method for monthly runoff prediction in the Weihe River Basin.
作者
邱绪迪
王坤
陈飞
相里宇锡
王斌
QIU Xudi;WANG Kun;CHEN Fei;XIANGLI Yuxi;WANG Bin(College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling 712100,China;State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China)
出处
《人民长江》
北大核心
2024年第8期79-86,95,共9页
Yangtze River
基金
国家自然科学基金项目(51509210)
陕西省重点研发计划项目(2021NY-181)。
关键词
径流预报
最优变分模态分解
随机配置网络
递归多步预测
渭河流域
runoff forecast
optimal variational mode decomposition
randomly configuring network
recursive multi-step prediction
Weihe River Basin