南水北调受水区工业需水量的多方法组合预测  

Multi-method combination prediction of industrial water demand in the receiving area of the South to North Water Diversion Project

作　　者：毛青 解阳阳 刘赛艳 席海潮 高峥 MAO Qing;XIE Yangyang;LIU Saiyan;XI Haichao;GAO Zheng(College of Hydraulic Science and Engineering,Yangzhou University,Yangzhou 225009,China;Jiangsu High Efficiency and Energy Saving Engineering Research Center for Large-scale Axial Flow Pump Stations,Yangzhou 226010,China;Modern Rural Water Conservancy Research Institute,Yangzhou 225007,China)

机构地区：[1]扬州大学水利科学与工程学院,江苏扬州225009 [2]江苏省高效节能大型轴流泵站工程研究中心,江苏扬州226010 [3]现代农村水利研究院,江苏扬州225007

出　　处：《水资源与水工程学报》2024年第3期51-58,66,共9页Journal of Water Resources and Water Engineering

基　　金：国家自然科学基金项目(52009116);江苏省高效节能大型轴流泵站工程研究中心开放课题(ECHEAP013);国家博士后科学基金项目(2018M642338);江苏省自然科学基金项目(BK20200958、BK20200959)。

摘　　要：以南水北调东线江苏段受水区为对象,2020年为现状年,在趋势法、多元线性回归和BP神经网络的基础上,利用相对误差-反距离权重法构建了一种组合模型。采用该组合模型预测江苏段受水区2030年工业需水量。结果表明:基于趋势法、多元线性回归和BP神经网络所得的工业需水量预测值之间的偏差均小于10%,各单一方法与真实值之间的平均误差均小于10%;组合预测模型所得工业需水量的决定系数(R^(2))比单一需水预测模型高0.02~0.09;江苏段受水区2030年工业需水总量预测值为14.68×10^(8) m^(3),相比现状年工业需水总量增加了70.3%。研究成果不仅可为南水北调东线江苏段受水区提供可靠的工业需水量预测数据,也可为其他南水北调受水区工业需水量预测提供方法借鉴。Based on trend analysis,multiple linear regression and BP neural network,a combined model was established to predict the industrial water demand of 2030 in the receiving area of the East Route of the South-to-North Water Diversion Project in Jiangsu Province by the relative error-inverse distance weight method with 2020 as the current year.The results show that the deviation between the predicted values of industrial water demand based on trend analysis,multiple linear regression and BP neural network is less than 10%,and the average error between each method and the real value is less than 10%.The coefficient of determination(R^(2))of industrial water demand obtained by the combined prediction model is 0.02-0.09 higher than that of any individual prediction model.The predicted value of the total industrial water demand of Jiangsu section in 2030 is 14.68×10^(8) m^(3),an increase of 70.3%compared with the current year(2020).The results of this study can not only provide reliable prediction data for the East Route of the South-to-North Water Diversion Project in Jiangsu Province,but also provide a reference method for industrial water demand prediction in other receiving areas of the project.

关 键 词：工业需水预测 趋势法 回归分析法 BP神经网络 南水北调江苏段受水区 

分 类 号：TV213.4[水利工程—水文学及水资源]