2013Unpublished venueRequires access

Selecting Proper Method for Groundwater Interpolation Based on Spatial Correlation

Jie Chen, Zhang Hanting, Qian Hui, Wu Jianhua, Xuedi Zhang

Open publisher page 20 citations

Abstract

Interpolation technique is often used in analyzing groundwater flow and physiochemical parameter distribution. In order to select an optimal interpolation method to describe the spatial distribution of groundwater properties, the spatial correlations of parameters such as groundwater level, salinity and nitrate were studies, based on which the inverse distance weighting (IDW) and Kriging interpolation methods were compared by the semivariable function of ArcGIS. The results show that when the data of samples have high spatial correlation, Kriging is a reliable method for interpolation, IDW method is more suitable for data of weak spatial correlation. Furthermore, IDW focuses on the values of neighboring locations with special distance while Kriging puts emphasis on the whole trend.

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What this paper is about

Interpolation technique is often used in analyzing groundwater flow and physiochemical parameter distribution. In order to select an optimal interpolation method to describe the spatial distribution of groundwater properties, the spatial correlations of parameters such as groundwater level, salinity and nitrate were studies, based on which the inverse distance weighting (IDW) and Kriging interpolation methods were compared by the semivariable function of ArcGIS. The results show that when the data of samples have high spatial correlation, Kriging is a reliable method for interpolation, IDW method is more suitable for data of weak spatial correlation. Furthermore, IDW focuses on the values of neighboring locations with special distance while Kriging puts emphasis on the whole trend.

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Available abstract

Interpolation technique is often used in analyzing groundwater flow and physiochemical parameter distribution. In order to select an optimal interpolation method to describe the spatial distribution of groundwater properties, the spatial correlations of parameters such as groundwater level, salinity and nitrate were studies, based on which the inverse distance weighting (IDW) and Kriging interpolation methods were compared by the semivariable function of ArcGIS. The results show that when the data of samples have high spatial correlation, Kriging is a reliable method for interpolation, IDW method is more suitable for data of weak spatial correlation. Furthermore, IDW focuses on the values of neighboring locations with special distance while Kriging puts emphasis on the whole trend.

Key concepts: Kriging, Inverse distance weighting, Multivariate interpolation, Interpolation (computer graphics), Spatial correlation, Groundwater, Geostatistics, Weighting

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