Sodium is an integral part of water, and its excessive amount in drinking water causes high blood pressure and hypertension. 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. Universal kriging and Bayesian universal kriging are used to predict the sodium concentrations. Spatial simulated annealing is used to generate optimized sampling designs. 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). It is concluded that Bayesian universal kriging fits better than universal kriging. It is also observed that the universal kriging predictor provides minimum mean universal kriging variance for both adding and deleting locations during sampling design.
Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan;Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan;Department of Statistics, University of Klagenfurt, Klagenfurt, Austria;Faculty of Health Studies, University of Bradford, BD7 1DP Bradford, United Kingdom;Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom;Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan;Arriyadh Community College, King Saud University, Arriyadh 11437, Saudi Arabia;Department of Statistics, COMSATS Institute of Information Technology, Lahore, Pakistan
Recommended Citation:
Erum Zahid,Ijaz Hussain,Gunter Spöck,et al. Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater[J]. PLOS ONE,2016-01-01,11(9)