WPT-HPO-ELM径流多步预报模型研究  

WPT-HPO-ELM multi-step runoff forecast model

作　　者：许建伟 崔东文 XU Jianwei;CUI Dongwen(Yunnan Institute of Water&Hydropower Engineering Investigation,Design and Research,Kunming 650021,China;Wenshan Prefecture Water Bureau of Yunnan Province,Wenshan 663000,China)

机构地区：[1]云南省水利水电勘测设计研究院,云南昆明650021 [2]云南省文山州水务局,云南文山663000

出　　处：《水资源与水工程学报》2022年第6期69-76,共8页Journal of Water Resources and Water Engineering

基　　金：云南省创新团队建设专项(2018HC024);云南重点研发计划项目(科技入滇专项);国家澜湄合作基金项目(2018-1177-02)。

摘　　要：为提高径流时间序列多步预报精度,建立了小波包变换(WPT)-猎人猎物优化(HPO)算法-极限学习机(ELM)相融合的径流时间序列多步预报模型,并应用于云南省南康河水文站月径流和日径流时间序列多步预报。引入HPO算法原理,在不同维度条件下选取6个典型函数对HPO进行仿真验证;利用2层WPT将径流时序数据分解为4个子序列分量,达到降低径流序列数据复杂性和不平稳性的目的;采用HPO优化ELM输入层权值和隐含层偏值,建立WPT-HPO-ELM模型对实例月径流和日径流进行多步预报。结果表明:HPO算法具有较好的寻优精度和全局搜索能力;WPT-HPO-ELM模型对预见期为1~3个月的月径流具有理想的预报效果,预报的平均绝对百分比误差≤2.43%,合格率≥99.2%,确定性系数≥0.999;对预见期为4~6个月的月径流具有较好的预报效果,预报的平均绝对百分比误差≤15.0%,合格率≥73.3%,确定性系数≥0.991;当预见期≥7个月时,预报效果较差。对预见期为1~3 d的日径流具有理想的预报效果,预报的平均绝对百分比误差≤1.23%,合格率为100%,确定性系数≥0.999;对预见期为4~7 d的日径流具有较好的预报效果,预报的平均绝对百分比误差≤15.3%,合格率≥73.0%,确定性系数≥0.947;当预见期≥8 d时,预报效果较差。WPT-HPO-ELM模型能充分发挥WPT、HPO和ELM的优势,表现出较高的预报精度和稳定性能,预报误差随着预见期的增加而增大,该模型及方法可为径流时间序列多步预报提供新途径。To improve the multi-step forecast accuracy of runoff time series,a novel model was established combining wavelet packet transform(WPT),hunter-prey optimization(HPO)algorithm and extreme learning machine(ELM),which was then applied to the multi-step forecast of monthly and daily runoff time series of the Nankang River Hydrological Station in Yunnan Province.The principle of HPO algorithm is introduced,and 6 typical functions are selected to simulate and verify HPO under different dimensional conditions.Then the data of runoff time series is decomposed into 4 subsequence components using double-layer WPT,so as to reduce the complexity and instability of the runoff sequence data.The ELM input layer weights and hidden layer biases are optimized to establish a WPT-HPO-ELM model for the prediction of monthly and daily runoff in multiple steps.The results show that the HPO algorithm has good optimization accuracy and global search ability;the WPT-HPO-ELM model performs ideally at the forecast of 1-3 months monthly runoff,with the mean absolute percentage error≤2.43%,the pass rate≥99.2%,and the coefficient of certainty≥0.999;it can also present a satisfactory result at the forecast of the monthly runoff with a forecast period of 4-6 months,with the mean absolute percentage error≤15.0%,the pass rate≥73.3%,and the coefficient of certainty≥0.99;however,it performed poorly when the forecast period is≥7 months.It has an ideal forecasting effect for the daily runoff with a forecast period of 1-3 d,with the mean absolute percentage error≤1.23%,the pass rate=100%,and the coefficient of certainty≥0.999;it also performs satisfactorily when forecast period is 4-7 d,with the mean absolute percentage error≤15.3%,the pass rate≥73.0%,and the coefficient of certainty≥0.94;whereas when the forecast period is≥8 d,the forecast effect is poor.The WPT-HPO-ELM model can give full play to the advantages of WPT,HPO and ELM,showing high forecast accuracy and stability;however,the forecast error increases with the incre

关 键 词：径流预报 小波包变换 猎人猎物优化算法 极限学习机 多步预报 仿真测试 

分 类 号：TV121.4[水利工程—水文学及水资源] P333[天文地球—水文科学]