灰色理论在新乡百泉泉水流量动态分析中的应用  

Application of the grey theory to dynamic analyses of the Baiquan Spring flow rate in Xinxiang

作　　者：李志超 姜宝良 潘登 卢金阁 王秀明 LI Zhichao;JIANG Baoliang;PAN Deng;LU Jinge;WANG Xiuming(College of Geosciences and Engineering,North China University of Water Resources and Electric Power,Zhengzhou,Henan 450046,China;Henan Provincial Institute of Natural Resources Monitoring and Land Management,Zhengzhou,Henan 450016,China;Hekou Village Reservoir Management Bureau,Henan Province,Jiyuan,Henan 459010,China;Jiyuan Hydrology and Water Resources Survey Bureau,Jiyuan,Henan 459099,China)

机构地区：[1]华北水利水电大学地球科学与工程学院,河南郑州450046 [2]河南省自然资源监测和国土整治院,河南郑州450016 [3]河南省河口村水库管理局,河南济源459010 [4]济源水文水资源勘测局,河南济源459099

出　　处：《水文地质工程地质》2023年第2期34-43,共10页Hydrogeology & Engineering Geology

基　　金：中国地质调查局地质调查项目(WT2019188B)。

摘　　要：新乡百泉具有供水、农灌、人文、旅游及生态等多种功能,研究其泉水流量动态,建立泉水流量动态预测模型,对泉域水资源评价和泉水资源保护具有重要意义。为了进一步研究新乡百泉天然状态下泉水流量动态特征、评价泉域岩溶水资源,基于1964—1978年泉水年均实测流量和泉域年均降水量资料,通过逐步回归分析,确定泉水流量的主要影响因素为前1年降水量,并建立了逐步回归模型,其回归效果显著;在逐步回归分析的基础上建立了泉水流量动态预测的GM(1,2)模型、NSGM(1, 2)模型和GM(0, 2)模型。结果表明:1964—1978年百泉泉水流量动态主要受泉域降水控制,且泉水流量滞后降水1年,反映了天然状态下泉水动态特征。3种灰色模型的精度等级均为最高级(优)。1964—1978年百泉泉水实测流量为2.347~6.448 m^(3)/s,平均为3.904 m^(3)/s;逐步回归模型预测值为1.882~6.383 m^(3)/s,平均为3.904 m^(3)/s;GM(1,2)模型预测值为2.327~6.448 m^(3)/s,平均为3.939 m^(3)/s;NSGM(1,2)模型预测值为2.133~6.448 m^(3)/s,平均为3.927 m^(3)/s;GM(0,2)模型预测值为1.787~6.448 m^(3)/s,平均为3.907 m^(3)/s。逐步回归模型和前述3种灰色模型的平均相对误差分别为7.794%、7.292%、7.122%、7.797%,均<10%,可用于泉水流量动态预测;其中NSGM(1,2)模型精度更高、对曲线“拐点”的拟合更好。根据4种模型预测的1964—2030年泉水流量,从保泉角度评价百泉泉域岩溶水的开采资源量不得超过1.69 m^(3)/s。研究成果可为泉水流量动态预测和泉域水资源评价提供科学依据,也可为类似地区地下水动态研究提供参考。The Baiquan Spring in Xinxiang has many functions, such as water supply, agricultural irrigation,humanities, tourism and ecology. It is of great significance to study the dynamics of the spring flow rate and establish a dynamic prediction model for the spring water resources evaluation and protection. In order to further study the dynamic characteristics of the Baiquan Spring flow rate in Xinxiang and evaluate karst water resources in the spring area, based on the data of annually measured spring flow rate and annual average precipitation in the spring area from 1964 to 1978, the main influencing factors of the spring flow rate are determined by using the stepwise regression analysis, and a stepwise regression model is established, with remarkable regression effect. On the basis of the stepwise regression analysis, this paper establishes the GM(1, 2) model, NSGM(1, 2) model and GM(0, 2) model for the dynamic prediction of the spring flow rate. The results show that(1) from 1964 to 1978,the Baiquan spring flow rate was mainly controlled by the precipitation in the spring area, and the spring flow rate lagged behind the precipitation for one year, reflecting the dynamic characteristics of the spring water in the natural state.(2) The accuracy levels of the three grey models are the highest(excellent).(3) From 1964 to 1978,the measured discharge of the Baiquan spring ranged from 2.347 to 6.448 m^(3)/s, with an average of 3.904 m^(3)/s. The predicted values of the stepwise regression model range from 1.882 to 6.383 m^(3)/s, with an average of 3.904 m^(3)/s.The predicted value of the GM(1, 2) model varies between 2.327 and 6.448 m^(3)/s, with an average of 3.939 m^(3)/s.The predicted values of the NSGM(1, 2) model range from 2.133 to 6.448 m^(3)/s, with an average of 3.927 m^(3)/s. The predicted values of the GM(0, 2) model range from 1.787 to 6.448 m^(3)/s, with an average of 3.907 m^(3)/s.(4) The average relative errors of the stepwise regression model and the three grey models mentioned above are 7.794%,7.292%, 7.1

关 键 词：新乡百泉 逐步回归分析 GM(1 N)模型 NSGM(1 N)模型 GM(0 N)模型 P-Ⅲ型曲线 

分 类 号：P641.8[天文地球—地质矿产勘探]