用多元统计方法评价樟子松成熟林生态系统的若干水质参数

IF 0.8 Q3 FORESTRY
I. Yurtseven
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引用次数: 0

摘要

本文采用多种统计解译方法对Coruh河支流Oltu河3个不同试验站的水质数据进行建模。在研究中使用的数据集中,径流量(Q)、水温(WT)、pH、电导率(EC)、钠(Na +)、钾(K +)、钙(ca2 +)和镁(Mg 2+)、碳酸盐(CO 32 -)、碳酸氢盐(HCO 3 -)、氯化物(Cl -)、硫酸盐(SO 42 -)、钠吸收因子(SAR)和硼(B)浓度的测量结果都存在。在2003年至2008年的5年期间,对每月测量结果组成的数据集应用了主成分分析(PCA)和多元回归分析(MLR)。PCA解释了水文和理化参数之间的关系,并对其进行了检验,由此产生的6个因子组产生了数据集总方差的90.7%。根据分析结果,径流与其他参数(电导率、钠、氯化物、硫酸盐、钠吸收因子和硼浓度)之间存在较强的负相关关系。研究发现,径流是一个重要的水文参数。采用多线性回归法确定估计方法。建立了径流与相互关系较强的参数间的估算模型。采用决定系数和均方误差(MSE)等指标对模型的性能进行了检验,结果令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Some Streamwater Quality Parameters Using with Multiple Statistical Methods in Mature Pinus sylvestris L. Forest Ecosystems
This study contains modeling water quality data from 3 different experiment stations of Oltu stream, which one of the tributaries of the Coruh stream has been provided by interpreting multiple statistical methods.  In the data set used in the study, runoff (Q), water temperature (WT), pH, electric conductivity (EC), sodium (Na + ), potassium (K + ), calcium(Ca 2+ ) and magnesium (Mg 2+ ), carbonate (CO 3 2- ), bicarbonate(HCO 3 - ), chloride (Cl - ), sulfate (SO 4 2- ), sodium absorption factor (SAR), and boron (B) concentration results of measurements were present. In the 5 year period between 2003 and 2008, Principal Component Analysis (PCA) and Multiple Regression Analysis (MLR) have been applied to the dataset which was composed of the monthly result of the measurement. PCA were explained to relations between hydrologic and physiochemical parameters and it were examined 6 factor groups created as a result of this examination was generated 90.7% of the whole variance of the data set. According to the results of the analysis, some strong negative relations between the runoff and some other parameters (electric conductivity, sodium, chloride, sulfate, sodium absorption factor, and boron concentration) were found. The runoff has been found as a hydrological parameter working as the key consideration.  The estimation method was determined by MLR. The estimation model has been developed among the runoff and those parameters which have strong relations with each other. The performance of this model was tested by using such criteria as coefficient of determination and Mean Squared Error (MSE) method and the results were found to be satisfactory.
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