Immune programming models of Cryptosporidium parvum inactivation by ozone and chlorine dioxide

This paper presents analytical models of Cryptosporidium parvum inactivation that have been evolved using immune programming. The objective of these models is to predict the reduction of infectivity associated with the disinfection by ozone and chlorine dioxide. To solve this problem, we introduce a modified immune programming approach together with corresponding implementation of the immune algorithm. The modeling results indicate that models obtained with immune programming outperform the traditional temperature corrected Chick–Watson models, as well as previously developed artificial neural network models. Detailed analysis of modeling errors, prediction power, and behavior of the models are included. Obtained models reveal that some input attributes have no effect on the prediction performance. This finding corresponds to the results previously obtained by saliency analysis of neural models. Results obtained in this study suggest that immune programming is becoming a mature technology which is ready for wide implementation in applications.