*3.3. Principal Component Analysis (PCA)*

PCA is one of the popular statistical analysis techniques that can be used to investigate data patterns. The Principal Components Approach can be assumed as a comprehensive Factor Analysis method. The goal of principle component analysis (PCA) is to construct new variables, known as principal components, from a set of existing original variables [59,60]. The new variables are created by linearly combining the current variables. The PCA reduces an extensive data set of variables into a few elements known as the principal components, which can then be analyzed to show the underlying data structure. It is one of the features of primary components that they are not correlated or orthogonal with one another. When a data set has a significant variance, the first principal component (F1) absorbs and accounts for as much variance as feasible. The second component (F2) absorbs the remaining variation as feasible, and so on. The maximum number of PCs or principal components equals the total number of variables in a model unless otherwise specified. Because each standardized variable has one variance, the total variance accounted for by all of the Fi's will be equal to the number of variables. Only a few Fi numbers are maintained in the data processing process to facilitate comprehension. The Kaiser criterion determines the number of primary components preserved in the analysis. It is also possible to express the latent root as a proportion of the overall variance in the data set. The diagram showing methodology is given in Figure 2. *Water* **2022**, *14*, x FOR PEER REVIEW 6 of 20

**Figure 2.** Flow chart of methodology used in the study. **Figure 2.** Flow chart of methodology used in the study.

it is way beyond the recommended value.

**4. Results and Discussion**  *4.1. Water Quality Index* 

**Table 1.** Water Quality Index.

is good if it falls between 26 and 50. Some other ranges, including 51–75 (poor), 76–100 (very poor), and >100 (Unfit for consumption). The calculated WQI of this area is 900, and

**Parameters** *Vn V0 Sn Wn Qn WQI*  Cr 0.015 0 0.05 20.000 29.523 29.523 Cu 0.002 0 2 0.500 0.079 0.079 K 8.964 0 20 0.050 44.820 44.820 Mn 0.002 0 0.4 2.500 0.589 0.589 Zn 0.005 0 3 0.333 0.156 0.156 Ba 0.166 0 0.7 1.429 23.704 23.704 As 0.022 0 0.01 100.000 220.996 220.996 TDS 863.049 0 500 0.002 172.610 172.610 EC 1478.488 0 400 0.003 369.622 369.622 NO3 1.410 0 5 0.200 28.200 28.200 SO4 23.570 0 250 0.004 9.428 9.428 pH 6.519 7 8.5 0.118 −32.065 −32.065 HCO3 87.546 0 350 0.003 25.013 25.013 Total 900.52
