## 4 Results and Discussion
### Discriminant Analysis  
Discriminant analysis (DA) was used to evaluate the spatial variations in water quality and to distinguish the most critical parameters in relation to variations between clusters. Both the standard and stepwise modes were applied to the primary data by dividing them into wet and dry seasons, and the two spatial groups resulting from CA were used in DA. In this case, the WQ parameters were treated as independent variables, while the clusters were considered as dependent variables. The confusion matrixes (CM) showed that 100%, 100%, and 100% of the data points were correctly classified in the standard, forward stepwise, and backward stepwise modes for both dry and wet seasons, respectively (Table 8). The standard DA method builds DFs using eighteen parameters, while only three and seven parameters were the critical parameters useful to make distinction within the two pollution groups for both the forward stepwise modes and backward stepwise modes, respectively, for both dry and wet seasons. In forward stepwise mode, most of the parameters such as turbidity, TDS, pH, NH\({}_{3}-\)N, NO\({}_{3}-\)N, PO\({}_{4}-\)P, DO, COD, NO\({}_{2}-\)N, TN, TP, temperature, Mg\({}^{2+}\), Ca\({}^{2+}\), and K\({}^{+}\) were insignificant variables leading to less variation, and they were deleted in the further process. However, in the forward stepwise DA \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monitoring** & **\% Correct** & \multicolumn{3}{c}{**Stations Assigned by DA**} \\
**Stations** & & **C1** & **C2** \\ \hline Standard DA mode for dry season & & & \\ C1 & 100 & 15 & 0 \\ C2 & 100 & 0 & 4 \\ Total & 100 & 15 & 4 \\ Standard DA mode for wet season & & & \\ C1 & 100 & 15 & 0 \\ C2 & 100 & 0 & 4 \\ Total & 100 & 15 & 4 \\ Forward stepwise DA mode for dry season & & & \\ C1 & 100 & 15 & 0 \\ C2 & 100 & 0 & 4 \\ Total & 100 & 15 & 4 \\ Forward stepwise DA mode for wet season & & & \\ C1 & 94 & 15 & 0 \\ C2 & 85 & 0 & 4 \\ Total & 84.5 & 15 & 4 \\ Backward stepwise DA mode for dry season & & & \\ C1 & 100 & 15 & 0 \\ C1 & 100 & 0 & 4 \\ Total & 100 & 15 & 4 \\ Backward stepwise DA mode for wet season & & & \\ C1 & 100 & 15 & 0 \\ C2 & 75 & 0 & 4 \\ Total & 87.5 & 15 & 4 \\ \hline \hline \end{tabular}  
* C1: Includes stations (MS1-MS3), MS6-MS14, and MS16-MS18). C2: Includes stations (MS4, MS5, MS15, and MS19).  
\end{table} 
Table 8: Classification matrix for standard, forward stepwise, and backward stepwise DA of spatial variation in LHW for both dry and wet seasons, showing percentage of correct assignation for discriminating parameters. mode, the three significant variables that were useful to make distinctions within the two pollution groups with 100% correct assignation were EC, BOD, and Na+. The backward stepwise mode deleted the least significant and identified seven significant variables: EC, DO, COD, TN, TP, Na+ and K+. These seven parameters, which were 100% correctly assigned, were the critical parameters useful to make distinctions within the two pollution groups. This implies that the expected spatial variation in water quality can be explained sufficiently using variables EC, DO, COD, TN, TP, Na+, and K+. Wilks' lambda shows that the discriminant distribution is skewed towards high concentrations. On the other hand, the standard DA functions was constructed using eighteen parameters, of which three and four parameters were used for forward stepwise mode and backward stepwise mode, respectively, for wet season. In forward stepwise mode, the pollutants that were found to be insignificant variables and had less variation in terms of their spatial distribution were deleted in the further process. However, in the backward stepwise DA mode, the three significant variables that were useful to make distinctions within the two pollution groups with 84.5% correct assignment were EC, Na+, and COD. The backward stepwise mode deleted the least significant and identified two significant variables: EC and Ca+2.