Prediction of Soil–Water Characteristic Curve Using Genetic Programming

Abstract

In this technical note, a genetic programming (GP) approach is employed to predict the soil–water characteristic curve (SWCC) of soils. The GP model requires an input terminal set that consists of initial void ratio, initial gravimetric water content, logarithm of suction normalized with respect to atmospheric air pressure, clay content, and silt content. The output terminal set consists of the gravimetric water content corresponding to the assigned input suction. The function set includes operators such as plus, minus, product, division, and power. Results from pressure plate tests carried out on clay, silty clay, sandy loam, and loam compiled in the SoilVision software were adopted as a database for developing and validating the genetic model. For this purpose, and after data digitization, GP software (GPLAB) provided by MATLAB was employed for the analysis. Furthermore, GP simulations were compared with the experimental results as well as the models proposed by other investigators. This comparison indicated superior performance of the proposed model for predicting the SWCC.