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In India, the management strategies for cleaning up of rivers are often not optimally prioritized and therefore, spatiotemporal monitoring of pollution levels becomes essential to devise effective measures for reclamation of the degraded urban water bodies (Farhad et al., 2013 Abba et al., 2015). Increased anthropogenic activities including direct discharge of untreated industrial effluents, domestic sewage and agricultural waste have severely degraded the quality of surface water bodies. İn recent years, water quality of major rivers, lakes and ponds in India has alarmingly deteriorated due to significant population increase leading to rapid urban development and industrialization. The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic ( F = 0.01) and p-value ( p = 0.994 > 0.05) revealed WQI estimates as “ not significant,” reasoned to the small water sample size (i.e., N = 40). WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R 2 ~ 0.81 and 0.67 for calibration and validation data, respectively. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R 2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as “ very poor” status. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R 2, RMSE and MAE. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination.
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A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery.