Artificial Neural Network Simulation of Combined Permeable Pavement and Earth Energy Systems Treating Storm Water

Abstract

Artificial intelligence techniques, such as neural networks, are modeling tools that can be applied to analyze urban runoff quality issues. Artificial neural networks are frequently used to model various highly variable and nonlinear physical phenomena in the water and environmental engineering fields. The application of neural networks for analyzing the performance of combined permeable pavement and earth energy systems is timely and novel. This paper presents the application of back-propagation neural networks and the testing of the Levenberg-Marquardt, Quasi-Newton, and Bayesian Regularization algorithms. The neural networks were statistically assessed for their goodness of prediction with respect to the biochemical oxygen demand (BOD), ammonia-nitrogen, nitrate-nitrogen, and ortho-phosphate-phosphorus by numerical computation of the mean absolute error, root-mean-square error, mean absolute relative error, and the coefficient of correlation for the prediction compared with the corresponding measured data. The three neural network models were assessed for their efficiency in accurately simulating the effluent water quality parameters from various experimental pavement systems. The models predicted all key parameters with high correlation coefficients and low minimum statistical errors. The back-propagation and feed-forward neural network models performed optimally as pollutant removal predictors with regard to these two sustainable technologies.