Estimation of the Earth Resistance by Artificial Neural Network Model

The aim of this paper is to investigate the estimation of the variation of ground resistance throughout the year by using artificial neural networks (ANNs). An ANN was trained, validated, and tested with different training algorithms by using experimental data of soil resistivity, ground resistance, and rainfall in order to select the optimum training algorithm and the respective parameters and predict the behavior of the ground resistance of a single rod. Moreover, a sensitivity analysis of the proposed ANN was carried out in order to determine the impact of certain factors on the efficiency of the ANN. The high value of the correlation index between estimated and experimental values demonstrates the high efficiency of the ANN. The proposed methodology based on ANN is a useful tool for the estimation of the grounding resistance during the year in case of difficulties in measuring its value.