Spatial representation of groundwater water data is usually produced using Geographical Information System (GIS) as a tool for groundwater management. This study aimed at investigating the effect of the GIS interpolation techniques on the accuracy of the spatial representation of such data. In this research, groundwater data (chloride concentration and water level) were collected from many wells along the Gaza Strip (GS). The data were then processed by GIS using three different interpolation techniques (e.g., Inverse Distance Weighting (IDW), Kriging, Spline). Statistical analysis using regression and residual analyses were applied for each interpolation technique to select the best fitted model. Then, cross-validation of the best fitted model was performed using two independent sets of data. Results showed that Kriging method produced the most accurate interpolating model for chloride concentration and for groundwater level prediction compared to IDW and Spline. It was therefore concluded that the Kriging method should be used in producing the surface maps for GS conditions to represent the two investigated parameters
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