Prediction of soil physical properties by optimized support vector machines

The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods

Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed

A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), generalized linear model (GLM), and multiple linear regression (MLR) models are appropriate for prediction of soil wet aggregate stability (as quantified by the mean weight diameter, MWD) in a highly mountainous watershed (Bazoft watershed, southwestern Iran). Three different sets of easily available properties were used as inputs. The first set (denoted as SP) consisted of soil properties including clay content, calcium carbonate equivalent, and soil organic matter content. The second set (denoted as TVA) included topographic attributes (slope and aspect) and the normalized difference vegetation index (NDVI). The third set (denoted as STV) was a combination of soil properties, slope, and NDVI. The ANN and ANFIS models predicted MWD more accurately than the GLM and MLR models. Estimation of MWD using TVA data set resulted in the lowest model efficiency values. The observed model efficiency values for the developed MLR, GLM, ANN, and ANFIS models using the SP data set were 60.76, 62.98, 77.68 and 77.15, respectively. Adding slope and NDVI to soil data (i.e. STV data set) improved the predictions of all four methods. The obtained correlation coefficient values between the predicted and measured MWD for the developed MLR, GLM, ANN, and ANFIS models using STV data set were 0.24, 0.35, 0.84 and 0.73, respectively. In conclusion, the ANN and ANFIS models showed greater potential in predicting soil aggregate stability from soil and site characteristics, whereas linear regression methods did not perform well.ISSN:0341-8162ISSN:1872-688