Symbolic Regression via Genetic Programming Model for Prediction of Adsorption Efficiency of some Pesticides on MWCNT/PbO2 Nanocomposite

In the present study, quantitative structure-property relationship (QSPR) model is developed for the adsorption efficiency (AE) of 70 pesticides in water sample on MWCNT/PbO2 solid phase extraction cartridge. Stepwise-multiple linear regression (SW-MLR) method are employed for selection of descriptors. The selected descriptors are MATS7v, MATS6c, GATS3s, ATSC6i, C040, SpMin8_Bhi, E2v, JGI1 and Mor08u. Further details of the effective descriptors indicate that the electronic, topological and geometrical characteristics of studied pesticides are the most effective parameters on their AE on MWCNT/PbO2 nanocomposite adsorbent. Symbolic regression via genetic programming (SR-GP) is utilized to offer the symbolic regression QSPR model. The accuracy and predictive power of the SR-GP model are compared with traditional linear and nonlinear regression models containing multiple linear regression (MLR) and support vector regression (SVR). Inspection the fitness parameters confirms the superiority of SR-GP model over MLR, and SVR models. In SR-GP model, the correlation coefficients (R) are 0.930 and 0.890, and the root mean square errors (RMSE) are 0.04 and 0.05 for the training and test sets, respectively. These results can be used to predict the AE for other pesticides by MWCNT/PbO2 adsorbent and designing a more efficient nano cartridge for SPE.