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Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater

Author

Listed:
  • Erum Zahid
  • Ijaz Hussain
  • Gunter Spöck
  • Muhammad Faisal
  • Javid Shabbir
  • Nasser M. AbdEl-Salam
  • Tajammal Hussain

Abstract

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.

Suggested Citation

  • Erum Zahid & Ijaz Hussain & Gunter Spöck & Muhammad Faisal & Javid Shabbir & Nasser M. AbdEl-Salam & Tajammal Hussain, 2016. "Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0161810
    DOI: 10.1371/journal.pone.0161810
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    References listed on IDEAS

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    1. Peter Diggle & Søren Lophaven, 2006. "Bayesian Geostatistical Design," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(1), pages 53-64, March.
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