## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Relative Weights (\(W_{\mathbf{w}}\))** & **Class** & **No. of Samples** \\ \hline \multirow{8}{*}{CatB} & \(W_{1}=0.019643\) & \(W_{11}=0.023257\) & Bad & 94,282 \\  & \(W_{2}=0.100292\) & \(W_{12}=0.038325\) & & \\  & \(W_{3}=0.129804\) & \(W_{13}=0.054098\) & & \\  & \(W_{4}=0.069898\) & \(W_{14}=0.015051\) & & \\  & \(W_{5}=0.061911\) & \(W_{5}=0.033832\) & Medium & 18,336 \\  & \(W_{6}=0.052939\) & \(W_{6}=0.08372\) & & \\  & \(W_{7}=0.030403\) & \(W_{17}=0.011317\) & & \\  & \(W_{8}=0.014039\) & \(W_{18}=0.019127\) & Good & 1332 \\  & \(W_{9}=0.012108\) & \(W_{19}=0.119901\) & & \\  & \(W_{20}=0.001063\) & & \\  & \(W_{10}=0.017696\) & \(W_{21}=0.019614\) & Poor & 6 \\  & \(W_{22}=0.154308\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Relative weights and the classified samples using CatB and LGBM methods on the test set. Figure 7: EWQI compared with NSF-WQI (with and without weight updates) on the test data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Weighting Method** & **No. of Parameters** & **Score** & **Accuracy** \\ \hline \multirow{8}{*}{CatB} & \multirow{8}{*}{7} & chl-\(\alpha\): 8.99274, & \multirow{8}{*}{78\%} \\  & & DO: 24.24110, & \\  & & EC: 29.92371, & \\  & & pH: 16.08041, & \\  & & SDD: 12.36481, & \\  & & TDS: 13.93459, & \\  & & Tur: 6.86264 & \\ \hline \multirow{8}{*}{LGBM} & \multirow{8}{*}{16} & chl-\(\alpha\): 604.0000 & Precipitation: 298.00000 \\  & & DO: 1235 00000 & Wind Speed: 301.00000 \\  & & EC: 993.00000 & CO: 718.00000 \\  & & pH: 1468.00000 & \(NO_{2}\): 973.00000 \\  & & SDD: 1212.00000 & \(O_{3}\): 1021.00000 \\  & & TDS: 1131.00000 & SO: 311.00000 \\  & & Tur: 494.00000 & HCHO: 785.00000 \\  & & Air Temperature: 311.00000 & \(CH_{4}\): 55.00000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Feature weighting method results on the training data for selected parameters. Figures 8 and 9 show the map for the classified Rawal stream network using both EWQI and NSFWQI. Test samples for 3 September 2018 and 2019 are classified as bad (shown in red) and medium (shown in orange) classes with EWQI, whereas the samples are mostly unclassified with the NSFWQI method. In addition to the 40% test data that are acquired for the monsoon months (June-September), 4998 sample points are collected from each non-monsoon or winter season of the year 2020. These data are used to further analyze the performance of the EWQI and are compared with the traditional NSFWQI. Table 7 shows the results for the test sets of six months, i.e., January, February, March, April, November and December 2020. The NSFWQI failed to classify the test subsets for these months of 2020. The parameters used for NSFWQI are the seven physico-chemical water quality parameters. The test sets for the year 2020 had some missing parameters, such as for November and December the meteorological parameters are missing, for January 2020 \(CH_{4}\) is missing. However, even with the missing parameters, the EWQI weights are applicable and have classified the data which is in contrast to the application of NSFWQI. Moreover, it can be seen that with EWQI throughout January to March, the classified samples have a 55:45 ratio for medium to bad class. However, for April this ratio shifted to 90:10. For November, the ratio further shifted to 45:55 and finally for December, the ratio was 10:90. The 10% to 90% ratio of medium to bad class indicates the river water pollution that occurs due to anthropogenic activities during winter [58]. This shows that the seasonal variations are visible with the EWQI method that is trained on the data collected for just monsoon months. Figures 10 and 11 display the test samples for January and February using the EWQI (LGBM) method. These classification maps are produced in ArcMap after applying post classification smoothing [59] using spatial analyst tools.