Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

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The present study investigates two important though relatively unexplored aspects of non-linear Bltration through porous media. The Brst aspect is the inCuence of viscosity variation over the coefBcients of the governing equations used for modelling non-linear Bltration through porous media. Velocity and hydraulic gradient data obtained for a wide range of Cuid viscosities (8.03E-07 to 3.72E-05 N/m 2 ) were studied. An increase in Cuid viscosity resulted in an increased pressure loss through packing which can be quantiBed using the coefBcients of the governing equations. CoefBcients of Forchheimer equation represent linearly increasing trend with the kinematic viscosity. On the other hand, coefBcient of Wilkins equation represents similar values for different Cuid viscosities and remained unaffected by the variation in packing properties. Obtained data were utilized to understand the nature of Cow transition in porous media. Behaviour of polynomial and Power-law coefBcient with variation in Cow velocity were also examined. Critical Reynolds number corresponding to the deviation of Cow from Darcy regime varies with the porous packing and was observed to be in the range of 0-100. CoefBcients of polynomial (Forchheimer) model were observed to be independent of the range of Cow velocity, whereas the Power law coefBcients are extremely sensitive to the data.

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Abstract:Behaviour of flow resistance with velocity is still undefined for post-laminar flow through coarse granular media. This can cause considerable errors during flow measurements in situations like rock fill dams, water filters, pumping wells, oil and gas exploration, and so on. Keeping the non-deviating nature of Wilkins coefficients with the hydraulic radius of media in mind, the present study further explores their behaviour to independently varying media size and porosity, subjected to parallel post-laminar flow through granular media. Furthermore, an attempt is made to simulate the post-laminar flow conditions with the help of a Computational Fluid Dynamic (CFD) Model in ANSYS FLUENT, since conducting large-scale experiments are often costly and time-consuming. The model output and the experimental results are found to be in good agreement. Percentage deviations between the experimental and numerical results are found to be in the considerable range. Furthermore, the simulation results are statistically validated with the experimental results using the standard 'Z-test'. The output from the model advocates the importance and applicability of CFD modelling in understanding post-laminar flow through granular media.

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