the PCA results for a multi layer aquifer (carbonate karst layer and alluvial aquifer are the most reservoir) gives three factors (eigenvalues above 1). the PC1 shows positive weightings with electrical conductivity, Cl, Na, Ca and negative weightings with HCO3. PC2 shows moderate positive weightings with pH and SO4 and moderate negative weightings with Mg. PC3 shows moderate negative weightings with K and moderate positive weightings with NO3 & pH. what is the meanning of the two last factors (PC2 & PC3). thank you in advance.
Hello Belkacem,
While someone more familiar with aquifer variables might "see" meaning in these arrangements, it is also entirely possible that there is none, since:
PCA arranges the variable set into linearly independent composite variables, such that the first one extracted will always account for the maximum amount of total variation in the data set, while successive components will account for successively lesser amounts. There's nothing inherent in the process that assures the resultant component/composite variates will necessarily be meaningful.
I'd strongly suggest you try rotation of the extracted components; this will frequently aid in trying to make sense of what each component represents. As well, you should set some criterion for the variable-component loading (correlation) to be declared "salient" for purposes of trying to characterize the meaning of each component. In other words, pay attention only to the variables that show salient relationships to a component when trying to characterize what that component might represent.
Good luck with your work!
Hello David,
Thank you so much for your response. the results are obtained after varimax rotation. only significant score coefficients (greater than 0.5) are taken in account in the interpretation. I'm trying to find some help because the PC2 and PC3 have an incomprehensive correlations.
thank you once again David for your pertinent comments.
Principal component analysis (PCA ) is a dimensional reduction tool that can be reduce a large set of variables to a small set that still contains most of the information in the large set. PC1 is the linear combination with the largest possible explained variations, and pc2 is the best of what is left. The data considered are Na,Cl, Ca, Mg, SO4, No3, pH and K?. This is all data based and require systematic study.
Hello Virendra Kumar Saxena,
Thank you so much for your response. TDS, electrical conductivity, Na, Cl, Ca, Mg, SO4, No3, HCO3, pH and K are the common physico-chemical parameters for the data set. detailed hydrochemical study is done but we have some hesitation in the PCA factors interpretation. we would like to have some ideas from scientists or experts working in similar cases.
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