Abstract
The aim of this study is to propose a method for constructing Artificial Neural Network (ANN) models and evaluating their performance based on the application of two methods for the selection of the ANN topology: the dynamic division method (cross-validation or dynamics-split) (DDM) and the static-split method (SSM). The two methods are compared and applied to predict the amount of organic matter in an up-flow anaerobic sludge blanket (UASB) reactor operated at full scale. The performance of the ANN models was assessed through the coefficient of multiple determination (R 2), the adjusted coefficient of multiple determination ( $R^{2}_{adj}$ ), and the root mean square error (RMSE). The comparison reveals that the DDM accurately selects the best model and reliably assesses its quality.
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