基于IPSO-ELM模型的调水工程输水损失预测方法研究  

Research on Water Loss Prediction Method of Water Transfer Project Based on IPSO-ELM Model

作　　者：赵然杭[1] 谢文泉 瞿潇 储燕 王兴菊[1] 李典基 ZHAO Ran-hang;XIE Wen-quan;QU Xiao;CHU Yan;WANG Xing-ju;LI Dian-ji(School of Civil Engineering,Shandong University,Jinan 250061,Shandong Province,China;Shandong South-to-North Water Diversion Project Builds Administration Bureau,Jinan 253000,Shandong Province,China)

机构地区：[1]山东大学土建与水利学院,山东济南250061 [2]南水北调东线山东干线有限责任公司,山东济南253000

出　　处：《中国农村水利水电》2023年第6期175-182,共8页China Rural Water and Hydropower

基　　金：山东省水利科研与技术推广项目(SDSLKY201807,SDSLKY201902)。

摘　　要：输水损失对调水工程的效益有重要影响,是调度计划制定的关键参数,准确预测输水损失对制定精细化调水方案,优化调度运行有重要意义。针对输水损失预测方法存在的局限性,提出一种计算精度优、构建效率高、适用范围广的输水损失预测方法:利用相关分析法和主成分分析法进行输水损失影响因素筛选,通过相关分析删除反映信息重复的指标,通过主成分分析筛选出重要性高的指标;基于筛选出的指标体系构建改进粒子群算法优化的极限学习机(IPSOELM)输水损失预测模型。以南水北调东线梁济运河段为例;经筛选,水深、流量、气温和风速为主要影响因素,建立IPSO-ELM输水损失预测模型;使用经过实测资料训练、验证后的预测模型,对不同情境下的数据进行日输水损失预测,并分别与极限学习机(ELM)模型和多元非线性回归(MNR)模型的预测结果对比分析。结果表明:IPSO-ELM输水损失预测模型计算的输水损失量和实际输水损失量十分接近,确定系数为0.9625,平均绝对百分比误差为1.322%,较MNR模型和ELM模型分别降低了52.89%和51.06%;预测误差主要分布在[-0.25,0.30]万m^(3)内,误差分布范围小于另外两个模型。即IPSO-ELM模型预测精度和泛化能力均优于另外两个模型,证明该方法是合理可行的,可用于计算不同调水情境下的输水损失,为水资源调度提供更加精确的水量信息。Water loss has an important impact on the efficiency of water transfer projects and is a key parameter in the formulation of scheduling plans,accurate prediction of water loss is of great significance for formulating refined water transfer plans and optimizing scheduling operations.Aiming at the existing problems of water loss prediction methods,this paper proposes a water transmission loss prediction method with high calculation accuracy,high construction efficiency and wide application scope:the correlation analysis method and principal component analysis method are used to screen the influencing factors of water loss,and the indicators reflecting the duplication of information are deleted through the correlation analysis,and the indicators with high importance are screened out by the principal component analysis;based on the filtered index system,a water transmission loss prediction model based on the improved particle swarm optimization extreme learning machine is constructed.The Liangji Canal section of the eastern line of the South-to-North Water Diversion Project is taken as an example.After screening,water depth,flow rate,air temperature and wind speed are the main influencing factors,and an IPSO-ELM water loss prediction model is established by using the predictive model trained and validated by the measured data,the daily water loss prediction is made on the data in different scenarios,and the prediction results of extreme learning machine model and the multivariate nonlinear regression model are compared and analyzed,respectively.The results show that the water loss and actual water loss calculated by the IPSO-ELM water transmission loss prediction model are very close,and the determination coefficient is 0.9625,and the average absolute percentage error is 1.322%,which is 52.89%and 51.06%lower than the MNR model and the ELM model,respectively.The prediction error is mainly distributed within[-25,30]thousand m^(3),and the error distribution range is smaller than the other two models.That is,the predicti

关 键 词：IPSO-ELM 输水损失 预测模型 调水工程 

分 类 号：TV68[水利工程—水利水电工程]