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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 f...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5040421/ https://www.ncbi.nlm.nih.gov/pubmed/27683016 http://dx.doi.org/10.1371/journal.pone.0161810 |
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author | Zahid, Erum Hussain, Ijaz Spöck, Gunter Faisal, Muhammad Shabbir, Javid M. AbdEl-Salam, Nasser Hussain, Tajammal |
author_facet | Zahid, Erum Hussain, Ijaz Spöck, Gunter Faisal, Muhammad Shabbir, Javid M. AbdEl-Salam, Nasser Hussain, Tajammal |
author_sort | Zahid, Erum |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5040421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50404212016-10-27 Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater Zahid, Erum Hussain, Ijaz Spöck, Gunter Faisal, Muhammad Shabbir, Javid M. AbdEl-Salam, Nasser Hussain, Tajammal PLoS One Research Article 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. Public Library of Science 2016-09-28 /pmc/articles/PMC5040421/ /pubmed/27683016 http://dx.doi.org/10.1371/journal.pone.0161810 Text en © 2016 Zahid et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zahid, Erum Hussain, Ijaz Spöck, Gunter Faisal, Muhammad Shabbir, Javid M. AbdEl-Salam, Nasser Hussain, Tajammal Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater |
title | Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater |
title_full | Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater |
title_fullStr | Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater |
title_full_unstemmed | Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater |
title_short | Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater |
title_sort | spatial prediction and optimized sampling design for sodium concentration in groundwater |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5040421/ https://www.ncbi.nlm.nih.gov/pubmed/27683016 http://dx.doi.org/10.1371/journal.pone.0161810 |
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