Multivariate statistics and entropy theory for irrigation water quality and entropy-weighted index development in a subtropical urban river, Bangladesh

Abstract

Currently, a well-developed combination of irrigation water quality index (IWQIs) and entropy water quality index (EWQIs) for surface water appraisal in a polluted subtropical urban river is very scarce in the literature. To close this gap, we developed IWQIs by establishing statistics-based weights of variables recommended by FAO 29 standard value using the National Sanitation Foundation Water Quality Index (NSFWQI) compared with the proposed EWQIs based on information entropy in the Dhaleshwari River, Bangladesh. Fifty surface water samples were collected from ve sampling locations during the dry and wet seasons and analyzed for sixteen variables. Principal component analysis (PCA), factor analysis (FA), Moran's spatial autocorrelation, and random forest (RF) model were employed in the datasets. Weights were allocated for preliminary variables to compute IWQI-1, 2 and EWQI-1, 2 respectively. The resultant IWQIs showed an analogous trend with EWQIs and revealed poor to good quality water, with IWQI-1 for the dry season and IWQI-2 for the wet season is further suggested. The entropy theory recognized that Mg, Cr, TDS, and Clfor the dry season and Cd, Cr, Cland SO 4 2for the wet season are the major contaminants that affect irrigation water quality. The primary input variables were lessened to ultimately shortlisted ten variables, which revealed good performance in demonstrating water quality status since weights have come effectively from PCA than FA. The results of the RF model depict NO 3 -, Mg, and Cr as the most predominant variables in uencing surface water quality. A signi cant dispersed pattern was detected for IWQImin-3 in the wet season (Moran's I>0). Overall, both IWQIs and EWQIs will generate water quality control cost-effective, completely objective to establish a scienti c basis of sustainable water management in the study basin. IntroductionWater quality means its quali cation for a particular reason and is controlled by class and number of disintegrated compositions (Ewaid et al. 2019). This composition acts a pivotal role in plant development and advancement either legitimately as far as insu ciency or in a roundabout way through in uencing supplement accessibility (Salem et al. 2019). In this way, top-notch harvests must be yielded by high-quality irrigation water as it is straightforwardly associated with soil and plant environment (Singh et al. 2018). Therefore, timely monitoring, appraising, and forecasting of probable variations in water quality are required (Matta et al. 2020;Islam et al. 2020a).Generally, simultaneous measurement of physical, chemical and biological water quality parameters is essential for a comprehensive surface water quality appraisal. One of the key potential issues of water quality studies is the set of variables that can be continuously monitored and related costing, collecting, analyzing and interpreting these datasets. To solve these issues, a particular water quality index (WQI) has been employed to handle the effective water quality classi cation using many parame...

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