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基于多元线性回归的表层岩溶泉流量预测
引用本文:严小龙,陈喜,张志才,刁贵芳.基于多元线性回归的表层岩溶泉流量预测[J].中国岩溶,2012,31(2):154-159.
作者姓名:严小龙  陈喜  张志才  刁贵芳
作者单位:1. 河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098;河海大学水文水资源学院,江苏南京210098
2. 南京水利科学研究院,江苏南京,210098
基金项目:国家自然科学基金项目(40930635、41101018、41001011),水文水资源与水利工程科学国家重点实验室自主课题(2009586412),博士点基金项目(20090094120008)
摘    要:利用贵州省安顺市普定县陈旗小流域泉流量动态变化及降雨观测资料,根据泉流量系列自相关和偏自相关以及降雨-泉流量系列互相关分析,确定泉流量系列以及降雨-泉流量响应滞时,建立泉流量与降雨及前期泉流量的多元线性回归模型。率定期(模型参数率定的时段)模型的输出结果与实测流量过程线较为吻合,效率系数为0.996,均方误差为3.0×10-7 m3/s,平均相对误差为2.12%。验证期效率系数为0.985,均方误差为3.96×10-7 m3/s,平均相对误差为5.36%。研究也同时表明,随着预测时段的增长,泉流量消退阶段的预测误差增大,因此文中所建模型用于10小时之内泉流量预测较为可靠,六场降雨后泉流量退水过程预测的泉流量相对误差平均值小于5%。

关 键 词:泉流量  相关函数  多元线性回归  降雨-流量模拟
收稿时间:2/6/2012 12:00:00 AM

Forecasting simulation for the discharge of epikarst spring on the basis of multiple linear regression
YAN Xiao-long,CHEN Xi,ZHANG Zhi-cai and DIAO Gui-fang.Forecasting simulation for the discharge of epikarst spring on the basis of multiple linear regression[J].Carsologica Sinica,2012,31(2):154-159.
Authors:YAN Xiao-long  CHEN Xi  ZHANG Zhi-cai and DIAO Gui-fang
Institution:1. State Key Lab of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu 210098, China 2. College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210098, China; 3. Nanjing Hydraulic Research Institute, Nanjing, Jiangsu 210098, China)
Abstract:In light of the spring discharge and the precipitation date in Chenqi drainage area in Puding County, Anshun City, Guizhou Province, the auto-correlation and partial auto-correlation of the epikarst spring discharge as well as cross-correlation of the discharge-precipitation series are analyzed. And then, the lag time of the discharge series and the precipitation-discharge lag time are determined, and the multiple linear regression model established. The output results of the calibration period fit the measured discharge series well, the nash efficiency coefficient (NEC) is 0.996, the root-mean-square error (RMSE) is 3.0×10-7 m3/s and the mean relative error (MRE) is 2.12%. The NEC of the validation period is 0.985, the RMSE is 3.96×10-7 m3/s, and the MRE is 5.36%. The results demonstrate that along with the increase of the prediction period, the prediction error of the spring discharge in subsiding phase increases. The model established in this paper is feasible for simulating the spring discharge in 10 hours, and the average value of the relative errors of the calculated discharge in subsiding phase after6 rainfalls is less than 5%.
Keywords:discharge of spring  related functions  multiple linear regression  precipitation and flow simulation
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