A Comparison of Some Interpolation Techniques for Determining Spatial Distribution of Nitrogen Compounds in Groundwater

Abstract

The aim of the study was to analyse spatial variability of selected parameters of subsurface waters in the area of approximately 10 ha, located in the valley of the Ciemięga River in the village of Snopków, near Lublin, Poland. For the purpose of this study, nine sections were delimited, each with four points of collecting groundwater. In the groundwater samples, there were measured NH + 4 , NO − 3 , and NO − 2 . Due to the small number of samples, the analysis was limited to deterministic interpolation methods. The following methods were compared using leave-one-out cross-validation procedure: triangulation, inverse distance weighting, radial base function, and modified Shepard's method. The methods which proved to be optimal were used to create spatial variability maps of the analysed parameters. Spatial interpolation and visualization of the results were performed in Surfer ver.16, and other calculations were conducted using R software. Article Highlights• The selection of an appropriate interpolation technique is the crucial factor in producing a reliable map of spatial variability in environmental research. • Both the spatial distribution and the number of the sample data being interpolated have to be included in the decision making process. • Radial basis function and modified Shepard's methods were superior to other methods in preserving the original sample point values. • Spatial interpolation by inverse distance weighting method performed among the poorest interpolators. • There are strong arguments for using simple interpolation techniques in the case of data sets spread over a wide range.

Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields

“…All of the parameters were interpolated using the Inverse Distance Weighted (IDW) interpolation technique. The IDW was preferred over other interpolation methods including kriging based on its simple approach [45] and several error statistics used to assess the performance of interpolation methods to represent the analysis of spatially continuous phenomena [46,47]. Since, lower values of the error statistics indicate higher accuracy of spatial interpolation, the lower error statistics of IDW were considered as basis for evaluating prediction accuracy of the interpolated data by this method [48].…”

Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression

“…Spatial interpolation for water quality mapping, such as inverse distance weighting, explicitly implements the assumption that the observations close together are more alike than those that are farther away [34,35]. Without considering the information of remote sensing, the spatial pattern of the Chla map (the result of spatial interpolation) is smoother [36].…”

The response of groundwater to multiple concerning drivers and its future: a study on Birbhum District, West Bengal, India

“…All of these predicted factors working as directly influencing drivers to groundwater table variability. To make thematic layers of concerning factor Inverse Distance Weighting or IDW method has been implied using spatial analyst tool in ArcGIS 10.5 software (Bronowicka-Mielniczuk et al 2019). IDW is a kind of interpolation method where missing values have been estimated by averaging other neighborhood sample values, and it assumes that closer values are more similar than the farthest value and it is used here to estimate the unknown stations' value (Manson et al 1999).…”