Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils

by Hasan Mozaffari, Ali Akbar Moosavi, Mohammad Amin Nematollahi

Hydraulic conductivity (K_ψ) is one of the most important soil properties that influences water and chemical movement within the soil and is a vital factor in various management practices, like drainage, irrigation, erosion control, and flood protection. Therefore, it is an essential component in soil monitoring and managerial practices. The importance of K_ψ in soil-water relationship, difficulties for its measurement in the field, and its high variability led us to evaluate the potential of stepwise multiple linear regression (SMLR), and multilayer perceptron (MLPNNs) and radial-basis function (RBFNNs) neural networks approaches to predict K_ψ at tensions of 15, 10, 5, and 0 cm (K₁₅, K₁₀, K₅, and K₀, respectively) using easily measurable attributes in calcareous soils. A total of 102 intact (by stainless steel rings) and composite (using spade from 0–20 cm depth) soil samples were collected from different land uses of Fars Province, Iran. The common physico-chemical attributes were determined by the common standard laboratory approaches. Additionally, the mentioned hydraulic attributes were measured using a tension-disc infiltrometer (with a 10 cm radius) in situ. Results revealed that the most of studied soil structure-related parameters (soil organic matter, soluble sodium, sodium adsorption ratio, mean weight diameter of aggregates, pH, and bulk density) are more correlated with K₅ and K₀ than particle-size distribution-related parameters (sand, silt, and standard deviation and geometric mean diameter of particles size). For K₁₅ and K₁₀, the opposite results were obtained. The applied approaches predicted K₁₅, K₁₀, K₅, and K₀ with determination coefficient of validation data (R²_val) of 0.52 to 0.63 for SMLR; 0.71 to 0.82 for MLPNNs; and 0.58 to 0.78 for RBFNNs. In general, the capability of the applied methods for predicting K_ψ at all the applied tensions was ranked as MLPNNs > RBFNNs > SMLR. Although the SMLR method provided easy to use pedotransfer functions for predicting K_ψ in calcareous soils, the present study suggests using the MLPNNs approach due to its high capability for generating accurate predictions.