Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran

Abstract

Selection of appropriate interpolation methods for the conversion of discrete samples into continuous maps is a controversial issue in the environmental researches. The main objective of this study was to analyze the suitability of three interpolation methods for the discrimination of groundwater with respect to the water quality index (WQI). The groundwater quality data consisted of 17 variables associated with 65 wells located in Andimeshk-Shush Plain. Three spatial interpolation methods including ordinary kriging (OK), empirical Bayesian kriging (EBK), and inverse distance weighting (IDW) were utilized for modeling the groundwater contamination. In addition, different cross-validation indicators were applied to assess the performance of different interpolation methods. The results showed that the performance differed slightly among different methods, although the best performed interpolation method in this study was the empirical Bayesian kriging. Among the interpolation methods, IDW with weighting power of 4 estimated the most contaminated area, while OK estimated the lowest contaminated area. The weighting power of IDW had a significant influence on the estimation, meaning that the estimated contaminated area was increased when a greater weighting power was selected. The subtraction results indicated that there are slightly spatial differences among the contamination assessment results. Results of both standard deviation (SD) and coefficient of variation (CV) also showed that uncertainty was highest in the southern part of the study area, where the distribution of wells were more intensive than that of the northern part.

Effect of Sampling Density on Estimation of Regional Soil Organic Carbon Stock for Rural Soils in Taiwan

“…On the contrary, EBK uses an intrinsic random function as the kriging model despite the other kriging methods. The other main difference of EBK with that of the other kriging model is that EBK does not assume a tendency toward an overall mean; thus, there is the same chance for large deviations to get larger or smaller [17].…”

“…Higher weighting values are assigned to those points, which are closer to the interpolated points. As the distance increases, the weight decreases, and u is the weighting power that imposes the amount of weight decreases with respect to the increase in distance [17,18].…”

“…During this step, the predictions and their respective standard errors are produced at the unsampled locations. This step finally creates a spectrum of semivariograms[17].…”

The effect of plant-soil treatment plant on the groundwater quality

“…Directly, during sampling, the groundwater level was measured. Statistically analysed were conducted with software package Statistica version 13.0 (Statsoft 2015) as well as deterministic spatial interpolation method (IDW -Inverse Distance Weighting) was applied [24,25]. Spatial interpolation was performed using the ArcGIS Spatial Analyst.…”

Application of several spatial interpolation techniques to monthly rainfall data in the Calabria region (southern Italy)

“…These methods include, among others, the use of some auxiliary variables, such as elevation and distance to coastline, to integrate in the multivariate geostatistics and the application of empirical Bayesian kriging (EBK) for spatial interpolation of monthly rainfall. Currently, the analysis of scientific literature shows that the use of EBK in studies dealing with the interpolation of hydrological variables can be found only in Mirzaei and Sakizadeh (2016) and Gupta et al (2017). In the latter case, EBK was compared to OK methods in the interpolation of rainfall data.…”