Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent (CCE), soil organic matter (SOM), and normalized difference vegetation index (NDVI). The results showed that the ANN model was more feasible in predicting the soil shear strength than the ANFIS model. The root mean square error (RMSE), mean estimation error (MEE), and correlation coefficient (R) between the measured soil shear strength and the estimated values using the ANN model were 0.05, 0.01, and 0.86, respectively. In ANFIS analysis, the RMSE was 0.08 and a lower correlation coefficient of 0.60 was obtained in comparison with the ANN model. Furthermore, the ANN and ANFIS models were more accurate in predicting the soil shear strength than was the conventional regression model. Results indicate that the ANN model might be superior in determining the relationships between index properties and soil shear strength.
Journal articles from the Grassland Society of Southern Africa (GSSA) African Journal of Range and Forage Science as well as related articles and reports from throughout the southern African region.