Comparative Study of Geospatial Techniques for Interpolating Groundwater Quality Data in Agricultural Areas of Punjab, Pakistan

Abstract

Groundwater Arsenic (As) data are often sparse and location-specific, making them insufficient to represent the heterogeneity in groundwater quality status at unsampled locations. Interpolation techniques have been used to map groundwater As data at unsampled locations. However, the results obtained from these techniques are affected by various inherent and external factors, which lead to uncertainties in the interpolated data. This study was designed to determine the best technique to interpolate groundwater As data. We selected ten interpolation techniques to predict the As concentration in the groundwater resources of Punjab, Pakistan. Two external factors, the spatial extent of the study area and data density, were considered to assess their impact on the performance of interpolation techniques. Our results show that the Inverse Distance Weighting (IDW) and Spline interpolation techniques demonstrate the highest accuracy with the lowest RMSE (13.5 ppb and 16.7 ppb) and MAE (87.8 ppb and 89.5 ppb), respectively, while the Natural Neighbor technique shows the lowest accuracy with the highest RMSE (2508.7 ppb) and MAE (712.1 ppb) to interpolate groundwater As data. When the study area’s extent was modified, IDW showed the best performance, with errors within ±1.5 ppb for 95% of the wells across the study area. While data density has a positive correlation with interpolation accuracy among all techniques, the IDW remained the best method for interpolation. It is therefore concluded that IDW should be used to interpolate groundwater quality data when observed data are sparse and randomly distributed. The utilization of IDW can be useful for As monitoring and management in groundwater resources.

Optimizing Interpolation Methods and Point Distances for Accurate Earthquake Hazard Mapping

“…where λ i is the kriging weight assigned to the observed value at location x i determined by the semivariogram. Higher weights show a greater influence on the observed data [63].…”