GIS-Based Spatial Interpolation Approaches for Soil Parameter Assessment: A Case Study from the Taza Khurmatu District, Kirkuk Governorate, Northern Iraq

Geotechnical applications, land management, and land use planning are generally concerned with the soil parameters of the studied areas in order to enable efficient land use. This study used an interpolation technique using Geographic Information System analysis to evaluate the quickly developing Taza Khurmatu District, southern Kirkuk Governorate. A total of thirty samples from different locations within the study area were analyzed using Inverse Distance Weighting and spline interpolation with a 10 x 8 m pixel size as the primary techniques. These techniques are used to find out about some of the most important soil characteristics such as density, moisture content, Liquid Limit, Plastic Limit, Plastic Index, and grain size. Weighted distance measurements for the various sampling techniques have been obtained utilizing the Inverse Distance Weighting technique, which assesses these parameters correctly. Several statistical indicators describing the precision of interpolated surfaces were used, including Root Mean Squared Error, Mean Absolute Error, together with the determination of the R² factor. In addition, Inverse Distance Weighting demonstrated high reliability in the parametric estimation of soil density and gravel content variations because these parameters showed substantial local variations. Inverse Distance Weighting demonstrated reliable performance in estimating soil characterization parameters since dry density delivered Root Mean Squared Error values of 0.21 along with R² of 0.87, and gravel content reached Root Mean Squared Error levels of 2.12 and R² of 0.90, indicating efficient local spatial structures depiction. The Spline interpolation generated precise modeling results for plastic index and clay content because it produced an Root Mean Squared Error of 2.34 and R² of 0.88 while effectively modeling smooth spatial transitions. Results from the current study validate how Geographic Information System-based interpolation techniques develop extensive soil property models for southern Kirkuk, which benefits environmental and urban planning operations. In addition to soil properties prediction, future applications should aim at expanding the number and size of the sample, including terrain attributes, and fitting satellite imagery and AI based models to increase the accuracy of the modeling of the generated maps.