基于多元线性回归的表层岩溶泉流量预测

严小龙; 陈喜; 张志才; 刁贵芳

doi:10.3969/j.issn.1001-4810.2012.02.007

基于多元线性回归的表层岩溶泉流量预测

doi: 10.3969/j.issn.1001-4810.2012.02.007

1.
河海大学水文水资源与水利工程科学国家重点实验室；河海大学水文水资源学院
2.
南京水利科学研究院

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)

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出版历程
- 收稿日期: 2012-02-06
- 发布日期: 2012-06-25

Forecasting simulation for the discharge of epikarst spring on the basis of multiple linear regression

1.
tate Key Lab of Hydrology Water Resources and Hydraulic Engineering, Hohai University；College of Hydrology and Water Resources, Hohai University
2.
Nanjing Hydraulic Research Institute

摘要

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3.0×10-7m3/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3.96×10-7m3/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%。
- 泉流量 /
- 相关函数 /
- 多元线性回归 /
- 降雨-流量模拟
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%.
- discharge of spring /
- related functions /
- multiple linear regression /
- precipitation and flow simulation

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参考文献(13)

[1]	张志才.西南喀斯特流域分布式水文模型及其应用［D］.南京:河海大学,2009.
[2]	朱生亮.表层岩溶水的时空分布及其变化规律［D］.贵阳:贵州大学,2009.
[3]	姜光辉,郭芳.我国西南岩溶区表层岩溶带的水文动态分析［J］.水文地质工程地质,2009,28(5):89-93.
[4]	覃小群,蒋忠诚.表层岩溶带及其水循环的研究进展与发展方向［J］.中国岩溶,2005,24(3):250-254.
[5]	蒋忠诚,王瑞江,裴建国,等.我国南方表层岩溶带及其对岩溶水的调蓄功能［J］,中国岩溶,2001,20(2):106-110.
[6]	Zaltsberg E A. Forecast of karst water levels［M］. Heise: Hydro-geology of Karstic Terraines, 1984: 40-42.
[7]	蒙海花,王腊春.岩溶流域水文模型研究进展［J］.地理科学进展,2010,(11):1311-1318.
[8]	周训.降雨量泉流量时间序列的谱分析［J］.勘察科学技术,1990,2:11-16.
[9]	张志才,陈喜,程勤波,等.喀斯特山体表层岩溶带水文地质特征分析——以陈旗小流域为例［J］.地球与环境,2011,39(1):19-25.
[10]	Chow V T, Maidment D R, Mays L W. Applied Hydrology［R］. McGraw-Hill: NewYork,1988.
[11]	Hsu K L, Gupta H V, Sorooshian S. Artificial neural network modeling of the rainfall-runoff process［J］. Water Resources Research,1995,31(10):2517-2530.
[12]	Hu C, Hao Y, Yeh T J, et al. Simulation of spring flows from a karst aquifer with an artificial neural network［J］. Hydrol. Process, 2008,22:596-604.
[13]	Kovacs A, Sauter M. Modelling karst hydro dynamics［C］// Methods in Karst Hydrogeology, eds. Nico Goldscheider & David Drew, International contribution to hydrogeology, IAH Master Series, 2007,26:201-222.