## 5 Results and Discussion  
Once the dataset is compiled and preprocessed using the methods mentioned in Section 4, the selected twenty-two parameters are used for calculating the EWQI. These include six air pollutants (CO, \(NO_{2}\), \(O_{3}\), \(SO_{2}\), HCHO and \(CH_{4}\)), six hydrological parameters (lithology, land use/land cover, soil type, slope, aspect and geology), three meteorological variables (air temperature, wind speed and total precipitation) and seven physico-chemical  
Figure 6: Meteorological parameters for the Rawal watershed. water quality features (TDS, pH, EC, SDD, DO, Tur and chl-\(\alpha\)). The selection of parameters is reassessed in the \"Weight Assignment\" stage using the tree-based algorithms. Next, the selected parameters are transformed using min-max normalization to a range of 0-100. The physico-chemical parameters, i.e., DO and chl-\(\alpha\) are measured in mg/L, while air pollutants are measured in mol/m\({}^{2}\). Thus, this step is necessary to obtain a uniform dataset. Then, a feature weighting technique is applied. For this step, the dataset is divided into 60:40% train and test sets. Both the training and test set results are mentioned. Moreover, with the 40% test set used to verify the proposed technique, a set of test data is acquired for the year 2020. This test set is taken to explore the EWQI results whether the index is functional under seasonal restrictions or there are certain missing parameters such as the state-of-the-art NSF method. It contains the days from other seasons besides the monsoon months that were originally used in the training dataset. This will help in the analysis and verification of the newly developed EWQI. The optimal number of clusters is four for the preprocessed train data. The clustering is performed to categorize the data samples as Class 1, 2, 3 and 4. Once the data is clustered and labelled, tree-based feature weighting is applied to obtain the parameter scores. Table 4 shows the weighting methods, scores and accuracy achieved on the training data. The best accuracy of 99.34% and 99.1% was achieved with the CatB and LGBM methods. The LGBM gave the best accuracy with 21 parameters, where the \"Geology\" parameter is discarded. The CatB method gave its best accuracy with the 22 parameters. Table 4 also shows the parameter scores of the feature weighting methods. XGB gave the highest scores to EC, SDD and lithology parameters. RF gave the highest scores to EC, TDS and geology, whereas LGBM gave SDD, pH, DO and O\({}_{3}\) the highest scores. Geology, EC, lithology and DO are the top scorers for CatB. This proves that multiple parameters play a part in categorizing the water. In order to test this hypothesis, the weighting methods were also tested for physico-chemical parameters alone and physico-chemical, air and meteorological parameters. Table 5 shows the results for the selected parameters for the top performing algorithms where the highest accuracy achieved was up to 82%. The dependencies of different parameters on the water quality can be seen with the inclusion of all 22 parameters that gave a 99% accuracy rate. The results of the classification achieved with the top two performing feature weighting techniques on the test set are given in Table 6. CatB weights classified the test set in four classes, i.e., bad (82.7%), medium (16%), poor (1.2%) and good (0.005%), whereas the LGBM classified the test data in two classes, i.e., bad (82.6%) and medium (17%). The test data was also classified using the traditional NSFWQI method. The weights in the NSFWQI were assigned based on the selection of the physico-chemical parameters. Thus, the NSFWQI weights need to readjusted for the current physico-chemical parameters used. The results of the classification of the test set using EWQI (CatB weighting), NSFWQI (without weight updates) and NSFWQI (with weight updates) is shown in Figure 7. Figure 7 represents the classification of samples with EWQI, NSFWQI (with weights updated) and NSFWQI (without weights updated).