![]() Received: AugAccepted: AugPublished: September 28, 2016Ĭopyright: © 2016 Zahid et al. Institute for Health & the Environment, UNITED STATES (2016) Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater. It is also observed that the universal kriging predictor provides minimum mean universal kriging variance for both adding and deleting locations during sampling design.Ĭitation: Zahid E, Hussain I, Spöck G, Faisal M, Shabbir J, M. It is concluded that Bayesian universal kriging fits better than universal kriging. ![]() Different estimation methods (i.e., maximum likelihood, restricted maximum likelihood, ordinary least squares, and weighted least squares) are used to estimate the parameters of the variogram model (i.e, exponential, Gaussian, spherical and cubic). Spatial simulated annealing is used to generate optimized sampling designs. ![]() Universal kriging and Bayesian universal kriging are used to predict the sodium concentrations. In the present paper, spatial distribution of sodium concentration in drinking water is modeled and optimized sampling designs for selecting sampling locations is calculated for three divisions in Punjab, Pakistan. Sodium is an integral part of water, and its excessive amount in drinking water causes high blood pressure and hypertension. ![]()
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