Abstract
Abstract: Nowadays, the interpolation methods have become an important technology on the groundwater research. Many geographic information system software based on different interpolator tools have been developed and used widely such as ArcGIS, MapInfo and ArcView. This study was conducted to evaluate interpolation tools for the prediction of HCO3, Cl, SO4, Ca and Na distribution in groundwater of northern regions of Khuzestan province. Inverse distance weighted, kriging, radial basis functions, local and global polynomial interpolation were five interpolation methods that used for this subject. 98 deep wells was selected and chemical analysis data was collected in summer 2008. Predicted values of contaminants were compared to observed data by RMSE, MAE (Mean Absolute Error) and MSDR (Mean Squared Standardized Deviation Ratio) indexes to select the optimum interpolator technique. The results show that the kriging method has the highest interpolation accuracy among five interpolation methods for mapping Ca, SO4 and HCO3 by RMSE equal to 0.56, 0.9 and 0.6 respectively. Also, RBF and IDW Methods have acceptable estimations for Cl and Na ions.
Highlights
Groundwater is one of the most important sources of drinking water in the world, especially in sparsely populated areas
Inverse distance weighted, kriging, radial basis functions, local and global polynomial interpolation were five interpolation methods that used for this subject. 98 deep wells was selected and chemical analysis data was collected in summer 2008
Predicted values of contaminants were compared to observed data by RMSE, MAE (Mean Absolute Error) and MSDR (Mean Squared Standardized Deviation Ratio) indexes to select the optimum interpolator technique
Summary
Groundwater is one of the most important sources of drinking water in the world, especially in sparsely populated areas. Many pollutants from agricultural, industrial and urban wastewater sources enter the groundwater and cause microbial and chemical pollution. Replacement of spatial variables instead of random variables and the expansion of GIS in relation to spatial statistics have introduced the use of interpolation methods in the preparation and analysis of zoning maps. This technology is used in estimating anonymous data in cases where sampling is difficult or the available information is not sufficient and accurate (Morio et al, 2010; Bardossy, 2011). The appropriate statistical method in estimating a variable depends on the type of variable and the factors affecting it and the selected method in one region can be generalized to other regions (Safari, 2002)
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