基于协同克里金的饮用水硝酸盐浓度预测研究

曹文翰1 ,张 强1 ,罗孝芹2 ,时 晓1 ,李红叶1

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中国农村水利水电 ›› 2018 ›› (6) : 84-87.
水环境与水生态

基于协同克里金的饮用水硝酸盐浓度预测研究

  • 曹文翰 ,张 强 ,罗孝芹 ,时 晓 ,李红叶
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Estimation Nitrate of Concentration in Drinking Water Based on Cooperative Kriging Interpolation

  • CAO Wen-han ,ZHANG Qiang ,LUO Xiao-qin ,SHI Xiao ,LI Hong-ye
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摘要

为了研究贵阳某机场机场附近居民饮用水源中硝酸盐浓度的空间分布情况,对 24个居民饮用水点分别进行普通克里金和协同克里金插值,并对插值结果进行比较分析。结果表明:通过距离模型判断出镁离子同硝酸盐的相关性最高,相关系数达到0.726但无法提供显著性P值;偏相关分析对两者相关性进行检验,相关系数为0.787,P=0.001表明两者相关性极强,可以将镁离子的浓度作为协变量进行协同克里金的插值;运用克里金插值时发现,协同克里金模型的标准平均值和标准均方根预测误差均优于普通克里金插值,表明将镁离子的浓度作为协变量,可以弥补主变量硝酸盐样本点少,变异函数欠稳定的缺点;通过协同克里金插值进而可以得到研究区内的硝酸盐浓度预测分布图,期待能给当地的水污染治理提供一定的指导价值。

Abstract

In order to study the spatial distribution of Nitrate concentration in drinking water sources of residents near an airport, the points of 24 residents were analyzed. The results showed that the correlation coefficient between magnesium and Nitrate was the highest, and the correlation coefficient was 0.726, but the significant P value could not be provided. The correlation between the two variables was tested by Partial process, and the correlation coefficient was 0.787, P = 0.001. The correlation between the two is very strong, and the concentration of magnesium ions can be used as a covariate for the interpolation of collaborative Kriging. When Kriging interpolation is used, the standard mean and standard root mean square prediction error of the cooperative Kriging model are both excellent. By kriging interpolation and then you can get the prediction of nitrate concentration in the study area Distribution, look forward to provide some guidance to the local water pollution control.

关键词

距离模型 / 偏相关分析 / 克里金插值 / 硝酸盐

Key words

Distance model / Partial model / CoKriging / Nitrate

基金

国家自然科学基金项目( 41472275)

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曹文翰1 ,张 强1 ,罗孝芹2 ,时 晓1 ,李红叶1. 基于协同克里金的饮用水硝酸盐浓度预测研究[J].中国农村水利水电, 2018(6): 84-87
CAO Wen-han1 ,ZHANG Qiang1 ,LUO Xiao-qin2 ,SHI Xiao1 ,LI Hong-ye1. Estimation Nitrate of Concentration in Drinking Water Based on Cooperative Kriging Interpolation[J].China Rural Water and Hydropower, 2018(6): 84-87

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