On the use of backpropagation neural networks in modeling environmental degradation.

Abstract

In this study a systematic analysis of the predictive capabilities of models built with backpropagation neural networks (BPNN) is made to corroborate the hypothesis that BPNN is capable of modeling the interaction terms in group contribution models, without explicitly adding these as descriptors. The data used for comparison are reactivities of 275 organic compounds towards the atomospheric OH-radical. This dataset was selected because of the internal consistency, reliability and relatively large size of this dataset. While training the network, the minimal Mean Squared Error (MSE) on a test set was used as the stop criterion. This avoids overfitting on the training data, and is most likely to give the best generalizing network. A network trained with a designed training and test set is compared with networks trained on randomly constructed training and test sets. The BPNN model based on designed training and test set not only gives the best model, but also the best predictability on an external validation set, compared both to linear models built with the same training and validation sets, and BPNN models based on randomly constructed training and test sets. The performance of the designed BPNN model is comparable to an existing model which includes interaction terms.