Author : Alibuyog, Nathaniel R.

Summary : Soil hydraulic properties and solute transport parameters are key inputs for models simulating water and chemical transport in soil. However, these properties are not easily obtainable because measuring them is resources intensive. One approach to address this data gap is through the development of pedotransfer functions. Thus, this study was carried out to develop soil water retention curve pedotransfer functions (SWRC-PTFs) and solute transport pedotransfer functions (ST-PTFs) using artificial neural network (ANN) to predict soil water retention and solute transport parameters, respectively, from basic soil physical and hydraulic properties. For comparison purposes, pedotransfer functions using linear and nonlinear regression analysis were also developed. Results showed that the SWRC-PTFs using neural network were adequate and formed a hierarchy in predicting soil water retention. Among the soil properties, bulk density was an important input variable in the model as it reflects the effect of soil structure on the flow of soil water. Its inclusion in the model increased the prediction of water retention by about 24%. On the other hand, the developed SWRC-PTFs using linear regression did not perform well. However, adjusting their regression coefficients using the global optimization approach significantly improved their performance by more than 117% compared with their linear versions and were comparable with the neural network SWRC-PTFs. The ST-PTFs also showed great potential in predicting soil pore velocity, dispersion coefficient and dispersivity.Finally, a computer program was developed to package six of the eight SWRC-PTFs and four ST-PTFs. This program provides a user-friendly interface for estimating soil hydraulic properties and solute transport parameters and more importantly has an improved accuracy and considerable degree of flexibility toward available input data.