Neural network models to predict soil water retention

Abstract

SummarySoil water retention curves are needed to describe the availability of soil water to plants and to model movement of water through unsaturated soils. Measuring these characteristics is time‐consuming, labour‐intensive and therefore expensive. This study was conducted to develop and evaluate functions based on neural networks to predict soil water retention characteristics. Dutch and Scottish data sets were available; they contained data on 178 and 165 soil horizons, respectively. A series of three neural networks (A, B and C) was developed. Neural network A had the following input variables: topsoil, bulk density, organic matter, clay, silt and sand content. In addition neural network B had matric potential as input, and network C included soil structural data expressed as the upper and lower boundary of the ped‐size class. Neural network A had three output variables: the volumetric water content at matric potentials of 0, –100 and –15 000 hPa. Both models B and C had volumetric water content, at the matric potential given as input, as output variable. The networks were tested against independent data that were extracted from the original sets of soil profiles. Accuracy of the predictions was quantified by the root of the mean squared difference (RMSE) between the measured and the predicted water contents, and the coefficient of determination (R2). For network A the RMSE varied for the three estimated water contents from 0.0264 to 0.0476 cm3 cm–3, and R2 varied from 0.80 to 0.93 for the individual model outputs. Networks B and C had an RMSE of 0.0435 and 0.0426 cm3 cm–3, respectively. For both networks, R2 was 0.89. The neural networks performed somewhat better than previous regression functions, but the improvements were not significant.