Predictive Model of Electrical Resistivity in Sandy, Silty and Clayey Soils Using Gravimetric Moisture Content

Soil electrical resistivity is a fundamental parameter in various geotechnical, agricultural, environmental, and engineering applications, as it directly depends on the soil’s moisture content and physical properties. Understanding this relationship is crucial for improving the safety and efficiency of electrical installations. This study analyzes the relationship between soil electrical resistivity and gravimetric moisture content in three soil types, sandy, clayey, and silty, with the aim of comparing the performance of different predictive models under controlled laboratory conditions. Seven fitting models were evaluated, Logarithmic Spline, Radial Basis Function (RBF), Locally Estimated Scatterplot Smoothing (LOESS), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RIDGE), Power Law and a segmented equation, using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination R2 . The Spline and RBF models showed excellent accuracy and near-zero errors in all soils, although their applicability is limited by the lack of an explicit formulation and by the fact that, as interpolation methods, they do not guarantee predictive capacity outside the experimental dataset. Therefore, their use should be restricted to controlled laboratory conditions, as field variability factors can significantly alter soil resistivity responses. Among the explicit models, the Segmented Equation obtained a MAPE of 6.14% (sandy), 15.1% (clayey), and 13.16% (silty), with R2 values of 0.91, 0.93, and 0.89, respectively, demonstrating good performance and functionality. The Power Law model, although showing an R2 close to 0.96, presented significant overestimations, especially in silty soils (MAPE > 187%). The LASSO model yielded inconsistent predictions with percentage errors exceeding 120% in silty soils. In conclusion, nonparametric models provide excellent accuracy, while the segmented equation stands out as the best explicit alternative for estimating resistivity with reasonable precision.