Spatial Interpolation of Sulfate Concentration in Groundwater Including Covariates Using Bayesian Hierarchical Models

Abstract

Sulfate is a key parameter for water quality and is commonly used in manufacturing of fertilizers, soaps, glass, papers, and common household items. If sulfate quantity is more than a threshold, it is hazardous for health. In the present paper, we use Bayesian kriging with external drift and Gaussian spatial predictive process model to analyze the spatial behavior of response variable (Sulfate). Different informative and non-informative priors are utilized to estimate the correlation parameters. The performance of these models are compared by means of twofold cross validation with deviance information criterion, and root mean square prediction as criterion. In summary, the inclusion of covariates plays an important role in minimizing the mean square prediction error. Bayesian kriging with external drift performs better than Gaussian spatial predictive process. The predictive distribution of Bayesian kriging with external drift is also applicable for interpolation of sulfate concentration at unobserved locations.