Artificial Neural Networks Approach for Electrochemical Resistivity of Highly Organic Soil

The resistivity of highly organic soils was measured using electrochemical resistivity reactor. Artificial neural networks (ANNs) were developed for the prediction of the resistivity at the different organic content, porosity, water content, and temperature. The results of study revealed that the resistivity of the highly organic soil decreased as the water content or temperature increased. The study showed that the resistivity of highly organic soil was also affected by degree of humification. As the degree of peat humification increased, the resistivity decreased. It was also concluded that the constructed ANNs models exhibited high performance for predicting of the resistivity of the highly organic soils.