A RBF Neural Network applied to predict soil Field Capacity and Permanent Wilting Point at Brazilian coast

The purpose of this paper was to evaluate the performance of pedotransfer functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at -30 kPa) and Permanent Wilting Point (PWP, -1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content and porosity. These data were taken from RURALDATA database, composed of 218 soil profiles. The RBF ANN was trained through the Radial Basis Learning algorithm. The ANN generated to predict FC and PWP for PROJIR present better performance than the other approaches presented in the related works. The performance of ANN to predict PWP was higher than to FC. Neural network models present similar performance to the previously developed regression-type and, in general, the addition of porosity, bulk density data and horizon type, did not improve the performance of ANN.