Prediction of molecular diffusivity of pure components into air: a QSPR approach.
The molecular diffusivity of 378 pure components into air was predicted using genetic algorithm-based multivariate linear regression (GA-MLR) and feed forward neural networks (FFNN). GA-MLR was used to select the molecular descriptors, as inputs for FFNN. The correlation coefficient (R2) of obtained multivariate linear seven-descriptor model by GA-MLR is 0.9334 and the same value for generated FFNN is 0.9643. These models can be applied for prediction of molecular diffusivity of pollutants into air in case of air pollution studies.
Department of Chemical Engineering, Faculty of Engineering, University of Tehran, PO Box 11365-4563, Tehran, Iran.
Mehdi
M
Sattari
Farhad
F
Gharagheizi
D000388:Air / Q000032:analysis; D000393:Air Pollutants / Q000737:chemistry*; D000397:Air Pollution / Q000706:statistics & numerical data*; D000465:Algorithms; D004058:Diffusion; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
D000393:Air Pollutants