Estimating Saturated Hydraulic Conductivity In Spatially Variable Fields Using Neural Network Ensembles

Modeling contaminant and water flow through soil requires ac-curate estimates of soil hydraulic properties in field scale. Although artificial neural networks (ANNs) based pedotransfer functions (PTFs) have been successfully adopted in modeling soil hydraulic properties at larger scales (national, continental, and intercontinental), the utility of ANNs in modeling saturated hydraulic conductivity (Ks) at a smaller (field) scale has rarely been reported. Hence, the objectives of this study are (i) to investigate the applicability of neural networks in estimating Ks at field scales, (ii) to compare the performance of the field-scale PTFs with the published neural networks program Rosetta, and (iii) to compare the performance of two different ensemble methods, namely Bagging and Boosting in estimating Ks. Datasets from two distinct sites are considered in the study. The performances of the models were evaluated when only sand, silt, and clay content (SSC) were used as in-puts, and when SSC and bulk density rb (SSC1 rb) were used as inputs. For both datasets, the field scale models performed better thanRosetta. The comparison of field-scale ANN models employing bagging and boosting algorithms indicates that the neural network model employing the boosting algorithm results in better generalization by reducing both the bias and variance of the neural network models. ESTIMATION of the hydraulic properties of soils is ofparamount importance for modeling contaminant and water flow through the vadose zone. Hydraulic properties also play an important role in partitioning the rainfall into runoff and soil moisture components. Soil hydraulic properties are usually measured in a labora-tory using representative soil samples from the study area. Since the hydraulic properties exhibit large var-iations within a spatial domain, large numbers of soil samples are required to characterize the hydraulic prop-erties of the study area. Laboratory estimates of hy-draulic properties are complex and time-consuming; therefore, the interest in using PTFs to estimate the hydraulic property of the soil is increasing (Rawls an