Principal Components Analysis (PCA) of Water Catchments in BUI Division North West Region of Cameroon: Implications on the Origin of Ions and Correlation

Fondzenyuy Vitalis Fonfo
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Abstract

Principal Component Analysis (PCA) helps in interpreting data and simplifies the complexity in high-dimensional or multivariate data while retaining trends and patterns. This is done by transforming the data into fewer dimensions that act as summaries of characteristics. The multivariate or high-dimensional data was prevalent in the physico- chemical analysis of samples from some water catchments in Bui division, North West Region of Cameroon. The water catchments were in Elak (ELA) Oku subdision, Mbiame (MBI) Mbven subdivision, Belem (BEL) Nkum subdivision, Nkar (NKA) Jakiri subdivision and YEH in Kumbo subdivision. This type of data presents several challenges that PCA mitigates. With the application of SPSS (Software Package for Social Sciences) it transformed the multivariables obtained in the physico-chemical analysis from the water catchments into a correlation matrix either Pearson’s or Spearman’s matrix. In this work I Pearson’s correlation matrix was used. It reduced the data by geometrically projecting onto lower dimensions in correlation circles. Pearson’s correlation matrix for the water catchments between the variables had a medium or moderate correlation with most of the coefficient values between -/+ 0.39 and +/- 0.49. Nonetheless, some strong positive correlations existed between Mg2+ / pH, TDS / EC, HCO3 - / T°C, and SiO2 / Mg2+, while a strong negative correlation existed between, Mg2+/ T°C, and Mg2+/ NO3 - . Generally, a weak correlation was observed between most of the variables. Eighty-seven variables were correlated from the catchments with 51 having a positive correlation, whereas 36 were negative. Positive correlation values indicated a common source with the ions evolving concomitantly by similar weathering processes or inputs from a common source. This concomitant correlation was expressed between Ca2+ and HCO3 - within the catchments. The negative values revealed that the ions evolve in an antagonistic manner. Consequently, in the water catchments, 58.62 % of the variables had a positive correlation while. 42.48 % had a negative correlation with antagonistic behaviour
喀麦隆西北地区BUI部门集水区的主成分分析(PCA):对离子起源和相关性的影响
主成分分析(PCA)有助于解释数据并简化高维或多变量数据的复杂性,同时保留趋势和模式。这是通过将数据转换为充当特征摘要的更少维度来实现的。在喀麦隆西北地区布伊省一些集水区的物化分析中,多元数据或高维数据普遍存在。集水区为Elak (ELA) Oku分区、Mbiame (MBI) Mbven分区、Belem (BEL) Nkum分区、Nkar (NKA) Jakiri分区和YEH in Kumbo分区。这种类型的数据提出了PCA可以缓解的几个挑战。应用SPSS(软件包的社会科学),它转换了从集水区的物理化学分析中获得的多变量到相关矩阵皮尔逊或斯皮尔曼的矩阵。在这项工作中,使用了皮尔逊相关矩阵。它通过几何投影到相关圆的较低维度来减少数据。各变量间集水区的Pearson相关矩阵具有中等或中等相关性,大部分系数值在-/+ 0.39和+/- 0.49之间。Mg2+/ pH、TDS / EC、HCO3 - / T°C和SiO2 / Mg2+之间存在较强的正相关关系,而Mg2+/ T°C与Mg2+/ NO3 -之间存在较强的负相关关系。一般来说,大多数变量之间的相关性较弱。87个变量与流域相关,其中51个为正相关,36个为负相关。正相关值表明有一个共同的来源,离子在相似的风化过程中同时演化或从一个共同的来源输入。在流域内Ca2+和HCO3 -之间表达了这种伴随相关性。负值表明离子以拮抗的方式演化。因此,在集水区,58.62%的变量呈正相关;42.48%与拮抗行为呈负相关
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