Neural Networks Prediction of Soil Hydraulic Functions for Alluvial Soils Using Multistep Outflow Data

Indirect methods for prediction of soil hydraulic properties play an important role in understanding site‐specific unsaturated water flow and transport processes, usually via numerical simulation models. Specifically, pedotransfer functions (PTFs) to predict soil‐water retention have been widely developed. However, few datasets that include unsaturated hydraulic conductivity data are available for prediction purposes. Moreover, those available employ a variety of measurement techniques. We show that prediction of soil‐water retention and unsaturated hydraulic conductivity curves from basic soil properties can be improved if hydraulic data are determined by a single measurement method that is consistently applied to all soil samples. Here, we present a unique dataset that consists of 310 soil‐water retention and unsaturated hydraulic conductivity functions, all of which were obtained from the multistep outflow method. With this dataset, neural networks coupled with bootstrap aggregation were used to predict the soil‐water retention and hydraulic conductivity characteristics from basic soil properties, that is, sand, silt, and clay content, bulk density (ρ b ), saturated water content, and saturated hydraulic conductivity. The prediction errors of water content were about 3 to 4% by volume. Unsaturated hydraulic conductivity predictions improved significantly when a so‐called performance‐based algorithm was utilized to minimize residuals of soil hydraulic data rather than hydraulic parameters. The root mean squared of residuals for predicted values of water content and unsaturated hydraulic conductivity were reduced by about 50% when compared with predicted hydraulic functions using a published neural networks program Rosetta Results from a sensitivity analysis suggest that the hydraulic parameters are mostly sensitive to sand content and saturated water content.