VMD-PSO-LSTM模型的日径流多步预测  

Research on multi-step forecast of daily runoff based on VMD-PSO-LSTM model

作　　者：王秀杰[1] 王玲[1] 滕振敏 田福昌 袁佩贤 苑希民[1] WANG Xiujie;WANG Ling;TENG Zhenmin;TIAN Fuchang;YUAN Peixian;YUAN Ximin(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300072,China;Zuojiang Drought Control Project Management Center of Chongzuo City,Chongzuo 532200,China;Beijing Urban Construction Design and Development Group Co.,Ltd.,Beijing 100032,China)

机构地区：[1]天津大学水利工程仿真与安全国家重点实验室,天津300072 [2]崇左市左江治旱工程管理中心,广西崇左532200 [3]北京城建设计发展集团股份有限公司,北京100032

出　　处：《水利水运工程学报》2023年第4期81-90,共10页Hydro-Science and Engineering

基　　金：国家重点研发计划资助项目(2018YFC1508403);科技部重点领域创新团队项目(2014RA031);国家自然科学基金委创新团队项目(51621092)。

摘　　要：为了弱化径流时间序列的非线性和非平稳性,提高不同预见期的日径流预测精度,提出了一种新的VMD-PSO-LSTM多步预测组合模型。首先采用变分模态分解(VMD)方法将原始日径流序列分解为子序列,通过粒子群优化算法(PSO)对长短期记忆(LSTM)模型参数进行优化,对各子序列建立PSO-LSTM模型,各分量的预测值重构集成预测结果。将VMD-PSO-LSTM模型应用于黄河下游花园口和利津站的日径流多步预测,采用Nash sutcliffe效率系数(ENS)、相关系数(R)和均方根误差(ERMS)3个定量评价指标对模型预测结果进行评价。结果表明:在预见期为1、2、3 d的情况下,两个测站的Nash sutcliffe效率系数和相关系数均在0.90以上。与CEEMD-PSO-LSTM和PSO-LSTM模型的预测结果对比表明,该模型能够有效提高日径流多步预测精度,是一种高效稳定的径流预报模型。In order to weaken the nonlinearity and non-stationarity of the runoff time series and improve the accuracy of daily runoff forecasts in different forecast periods,a new VMD-PSO-LSTM multi-step forecasting combined model is proposed.First,the variational modal decomposition(VMD)method is used to decompose the original daily runoff sequence into a set of sub-sequences,and the long and short-term memory(LSTM)model parameters are optimized by the particle swarm optimization algorithm(PSO),and each sub-sequence is established.In the PSO-LSTM model,the prediction results of each component are reconstructed to integrate the prediction results.The VMD-PSO-LSTM model is applied to the multi-step prediction of daily runoff at Huayuankou and Lijin Stations on the lower reaches of the Yellow River.Nash sutcliffe efficiency coefficient,correlation coefficient and root mean square error are used to evaluate the prediction results of the model.The results show that the ENS and R of the two stations are above 0.90 when the forecast period is 1 d,2 d and 3 d.The comparison with the prediction results of CEEMD-PSO-LSTM and PSO-LSTM models shows that this model can effectively improve the accuracy of multi-step daily runoff prediction.It is an efficient and stable runoff prediction model,and provides a new method for multi-step daily runoff.

关 键 词：非平稳序列 日径流多步预测 长短期记忆模型 变分模态分解方法 

分 类 号：TV124[水利工程—水文学及水资源]