Estimation of Soil Organic Carbon Normalized Sorption Coefficient (Koc) Using Least Squares‐Support Vector Machine

Abstract

AbstractLeast squares‐support vector machine (LS‐SVM) was used to derive a quantitative structure‐activity relationship (QSAR) model for predicting the soil sorption coefficient normalized to organic carbon, Koc, from 24 fragment‐specific increments and four further molecular descriptors, employing a training set of 571 organic compounds and three external validation sets. The combinational parameters of LS‐SVM were optimized by adaptive random search technique (ARST). ARST could search the optimal combinational parameters of LS‐SVM from the solution space in a simple and quick way. The developed LS‐SVM model was compared with the model established by multiple linear regression (MLR) analysis using the same data sets. Generally, the LS‐SVM model performed slightly better than the MLR model with respect to goodness‐of‐fit, predictivity, and applicability domain (AD). The ADs of the LS‐SVM and MLR models were described on the basis of leverages and standardized residuals. Both the LS‐SVM and MLR models had wide ADs within a given reliability (standardized residual<3 SE units), but the LS‐SVM model was superior for compounds with high leverages.