Spatial modeling of geotechnical soil parameters: Integrating ground-based data, RS technique, spatial statistics and GWR model

Abstract

Ensuring the availability of a cost-effective method to predict soil behavior is imperative for newly developed construction sites. This research aims to create a model that can estimate the spatial distribution of soil properties and compaction characteristics using the Inverse Distance Weighting (IDW) and Geographic Weighted Regression (GWR) methods within the study area, specifically in Koya city, situated in Erbil, Iraq. To determine these soil parameters, 27 soil samples were collected from the fields based on stratified random sampling, and then tested and analyzed in the laboratory. The IDW spatial interpolation technique and GWR method were then used to create a spatial distribution map of soil properties and compaction characteristics. In the GWR model, the calculated soil properties and compaction characteristics served as the dependent variable, while the Modified Normalized Difference Water Index (MNDWI) derived from the Landsat8 satellite image was the independent variable. This process resulted in a spatial distribution map showing the soil properties and compaction characteristics. The results indicated a strong correlation between the MNDWI water indexes and various soil parameters, including water content, liquid limit, plastic limit, optimum moisture content, and maximum dry density, with respective coefficient of determination (R²) values of 0.91, 0.97, 0.98, 0.95, and 0.96. Additionally, the assessment of the precision in this correlation indicates that the results maintain a satisfactory level of accuracy, as demonstrated by the Root Mean Square Error (RMSE) values, which are 2.86 for water content, 5.4 for liquid limit, and 3.85 for plastic limit, 2.9 for optimum moisture, and 13.86 for maximum dry density. By integrating satellite-derived MNDWI water indexes with soil parameters, a fast, accurate, and cost-effective method for estimating soil parameters and modeling their spatial distribution in the study area can be developed. Additionally, the findings suggest that the IDW method, implemented using spatial analyst tools, performed exceptionally well for mapping the study area. In conclusion, the results of this research can be utilized by land use planners, municipalities, policymakers, and engineers to develop practical and effective plans.