  Determining the relationship between the electrical resistivity of soil and
its geotechnical properties is an important engineering problem. This study
aims to develop methodology for finding the best model that can be used to
predict the electrical resistivity of soil, based on knowing its geotechnical
properties. The research develops several linear models, three non-linear
models, and three artificial neural network models (ANN). These models are
applied to the experimental data set comprises 864 observations and five
variables. The results show that there are significant exponential negative
relationships between the electrical resistivity of soil and its geotechnical
properties. The most accurate prediction values are obtained using the ANN
model. The cross-validation analysis confirms the high precision of the
selected predictive model. This research is the first rigorous systematic
analysis and comparison of difference methodologies in ground electrical
resistivity studies. It provides practical guidelines and examples of design,
development and testing non-linear relationships in engineering intelligent
systems and applications.
