基于小波包分解的EHO-ELM与EHO-DELM日径流多步预报模型研究  

Research on the Multi-step Forecast Models of EHO-ELM and EHO-DELM Daily Runoff Based on Wavelet Packet Decomposition

作　　者：李新华 崔东文 LI Xin-hua;CUI Dong-wen(Yunnan Xingdian Group Co.,Ltd.,Wenshan 663000,Yunnan Province,China;Yunnan Provincial Wenshan Water Bureau,Wenshan 663000,Yunnan Province,China)

机构地区：[1]云南兴电集团有限公司,云南文山663000 [2]云南省文山州水务局,云南文山663000

出　　处：《中国农村水利水电》2022年第10期81-86,共6页China Rural Water and Hydropower

基　　金：国家自然科学基金项目(91547205)。

摘　　要：为提高日径流时间序列多步预报精度,基于小波包分解(Wavelet Packet Decomposition,WPD)方法、象群优化(Elephant Herding Optimization,EHO)算法和极限学习机(Extreme Learning Machine,ELM)、深度极限学习机(Eeep Extreme Learning Machine,DELM)两种预测器,研究提出WPD-EHO-ELM、WPD-EHO-DELM日径流时间序列混合预报模型,并应用于云南省景东水文站日径流时间序列多步预报。首先利用2层WPD将日径流时序数据分解为4个子序列分量,达到降低日径流序列数据复杂性和不平稳性的目的;其次在延迟时间为1的情况下,采用Cao方法确定各子序列分量的输入向量;最后介绍EHO算法原理,分别利用EHO优化ELM、DELM输入层权值和隐含层偏值,建立WPD-EHOELM、WPD-EHO-DELM模型对各子序列分量进行多步预测,将预测结果加和重构得到最终日径流多步预报结果。并构建基于小波(Wavelet Decomposition,WD)分解的WD-EHO-ELM、WD-EHO-DELM模型和未经分解的EHO-ELM、EHODELM模型作对比分析模型。结果表明:(1)WPD-EHO-ELM、WPD-EHO-DELM模型对实例预见期为1~5 d日径流预报的平均绝对百分比误差≤9.44%,合格率≥89.2%,确定性系数≥0.99,精度等级均为甲级,预报效果均优于WD-EHO-ELM等其他模型。其中预见期为1~3 d日径流预报的平均绝对百分比误差≤1.81%、合格率100%,确定性系数≥0.999 6,预报效果最理想。(2)WPD-EHO-ELM、WPD-EHO-DELM模型能充分发挥WPD分解、EHO算法和ELM、DELM网络优势,表现出较高的预报精度和稳定性能,预报精度随着预见期天数的增加而降低。(3)模型及方法可为实现日径流时间序列多步预报和精准预报提供新途径。The multi-step forecast accuracy of daily runoff time series is improved. Based on the wavelet packet decomposition(WPD)method,the elephant group optimization(EHO)algorithm and the extreme learning machine(ELM)and deep extreme learning machine(DELM)two predictors,the WPD-EHO-ELM,WPD-EHO-DELM daily runoff time series mixed forecast model,and applied to the multistep forecast of daily runoff time series at Jingdong Hydrological Station in Yunnan Province. Firstly,the two-layer WPD is used to decompose the daily runoff time series data into 4 sub-sequence components to reduce the complexity and instability of the daily runoff series data.Secondly,when the delay time is 1,the Cao method is used to determine each sub-sequence component. Finally,this paper introduces the principle of EHO algorithm,uses EHO to optimize ELM,DELM input layer weights and hidden layer biases,and establishes WPD-EHOELM and WPD-EHO-DELM models to perform multi-step prediction of each sub-sequence component. And the prediction results are added and reconstructed to obtain the final multi-step forecast results of daily runoff. And the WD-EHO-ELM and WD-EHO-DELM models are constructed based on the wavelet(WD)decomposition and the undecomposed EHO-ELM and EHO-DELM models as comparative analysis models. The results show that:(1)WPD-EHO-ELM and WPD-EHO-DELM models have an average absolute percentage error of daily runoff forecast from 1d to 5 d for an example forecast period of ≤9.44%,pass rate ≥89.2%,and certainty coefficient ≥0.99,The accuracy grades are all Grade A,and the forecasting effects are better than other models such as WD-EHO-ELM. Among them,the forecast period is1~3 d. The average absolute percentage error of daily runoff forecast is less than or equal to 1.81%,the pass rate is 100%,and the certainty coefficient is greater than or equal to 0.999 6. The forecast effect is the most ideal.(2)WPD-EHO-ELM and WPD-EHO-DELM models can give full play to the advantages of WPD decomposition,EHO algorithm and ELM and DELM networks,and show

关 键 词：日径流预报 小波包分解 象群优化算法 极限学习机 深度极限学习机 多步预测 

分 类 号：TV213.4[水利工程—水文学及水资源]