## 6 Conclusions  
The physico-chemical, hydrological and topographical air pollutants and meteorological parameters were extracted from S2-MSI L1C, SRTM DEM, S5P-L2 and ERA5-CRP, respectively, for the Rawal stream network for the monsoon months (June to September) for the years 2018 to 2022. The water quality was assessed using WQI methodology to rank the water bodies. However, the application of the WQIs on water samples is a biased approach as each index is built specific to certain locations or water types or is sensitive to specific parameter concentrations or is dependent on the weights assigned. Such limitations make the traditional WQIs unsuitable for application on any general water body. Thus, this study aimed to determine the impact of other natural factors in the environment to understand and classify the water quality using an enhanced water quality index method. An enhanced indexing methodology is proposed that, compared to the traditional or state-of-the-art WQI, is based on a multitude of parameters and machine learning techniques. The first step of building the EWQI method was the parameter selection, where 22 physico-chemical, hydrological and topographical air pollutants and meteorological parameters were selected, i.e., lithology, geology, soil type, wind speed, air temperature, CO, \(NO_{2}\), \(O_{3}\), DO, TDS, etc. Next, the sub-index calculation was performed using the min-max normalization technique to transform the data in the 0 to 100 range. The third and most crucial step was assigning weights where the train data was clustered using the Elbow method to find the K value. The final weights were then calculated on the clustered train data with LGBM and CatB models giving a 99% accuracy. These weights were then assigned to the test data. Once the sub-index and weights were calculated, the sub-indices aggregation took place by applying the formula given in Equation (3). The final step was the classification of the EWQI values using the WHO ranking system. The conclusions drawn from the analysis of the newly proposed indexing technique are that the use of tree-based LGBM weighting and min-max normalization methods can lead to the accurate classification of the stream network as compared to the traditional NSFWQI. Moreover, the parameters, i.e., physico-chemical and other natural factors such as air pollutants, air temperature, slope, aspect, etc. all play a role in categorizing the water quality where EC, SDD, DO, lithology and geology are given high scores or weights with the feature weighting methods LGBM and CatB. Contrary to the NSFWQI, the missing parameters do not influence the classification of the water body using the EWQI. Even with more than five missing parameters for November and December 2020, the classification maps are produced with each sample assigned to a bad, medium or good class. The EWQI works well for all seasons as the seasonal variations can also be observed for January to December where the water quality class ratio shifted from 55:45 to 10:90 ratio for medium to bad class. In contrast, NSFWQI failed to classify the samples. Thus, the new and improved EWQI method will help remove the uncertainties involved in the traditional methods and can contribute to water management planning on a global scale. In the future, the EWQI can be explored further for other water bodies such as Khanpur, Mangla and Tarbela Dam. Conceptualization, M.A. and R.M. ; methodology, M.A. ; software, M.A. ; validation, M.A., R.M. and Z.A. ; formal analysis, R.M. ; investigation, M.A. ; resources, R.M. ; data curation, M.A. ; writing--original draft preparation, M.A. ; writing--review and editing, R.M. and Z.A. ; visualization, M.A, R.M. and Z.A. ; supervision, R.M. ; project administration, R.M. ; funding acquisition, Z.A. All authors have read and agreed to the published version of the manuscript. Funding is provided by the Sheila and Robert Challey Institute for Global Innovation and Growth at North Dakota State University, USA. **Informed Consent Statement:** Not applicable.