Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions

Saturated hydraulic conductivity (K_sat), one of the critical soil hydraulic properties, is used to model many soil hydrological processes. Measurement of K_sat on a routine basis is a labor-intensive, time-consuming, and expensive process. Alternatively, prediction of K_sat values from easy to obtain soil features is more economical and saves time. Artificial neural networks (ANNs) can be used to model and describe the most influential features affecting K_sat. This study aimed to develop and evaluate the potential use of generalized regression neural network (GRNN) to identify the optimal set of soil features to predict K_sat under arid and semi-arid environments. A total of 165 soil samples were collected from three depths (0–15, 15–30, and 30–60 cm) and analyzed for K_sat, texture, organic matter (OM), pH, bulk density (BD), and electrical conductivity (EC). Fourteen GRNN models were built with different feature combinations to identify the optimal set to predict K_sat. The results showed that soil texture explained 78% of the variability in soil K_sat while introducing EC improved model's ability to estimate soil K_sat (R = 0.93, MSE = 2.89 × 10^-12 m² S^-2). The optimum set of soil properties that should be included in the model were sand and clay percentages and EC values as evidenced from the cross-validation results. The GRNN model (using small dataset and set of features) provided reliable predictions of K_sat on bar with more complex models that included extensive set of features and used more extensive dataset. This work has implications for soil scients as provides an economical method to estimate K_sat values.