Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater.
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.
Erum
E
Zahid
Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.
Ijaz
http://orcid.org/0000-0002-1586-1503
I
Hussain
Department of Statistics, University of Klagenfurt, Klagenfurt, Austria.
Gunter
G
Spöck
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.
Muhammad
M
Faisal
Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.
Javid
J
Shabbir
Arriyadh Community College, King Saud University, Arriyadh 11437, Saudi Arabia.
Nasser
N
M AbdEl-Salam
Department of Statistics, COMSATS Institute of Information Technology, Lahore, Pakistan.
Tajammal
T
Hussain
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