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Artificial neural network simulation of combined permeable pavement and earth energy systems treating storm water

Artificial neural network simulation of combined permeable pavement and earth energy systems treating storm water

Tota-Maharaj, K. and Scholz, M. (2011) Artificial neural network simulation of combined permeable pavement and earth energy systems treating storm water. Journal of Environmental Engineering, 138 (4). pp. 499-509. ISSN 0733-9372 (Print), 1943-7870 (Online) (doi:https://doi.org/10.1061/(ASCE)EE.1943-7870.0000497)

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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.

Item Type: Article
Additional Information: [1] Acknowledgements (funding): The authors wish to thank Hanson Formpave, part of the Heidelberg Cement Group, for providing financial support for this research. [2] Acknowledgements (support): Support provided by Stephen Coupe (Coventry University) and Piotr Grabowicki (Environment Agency) is acknowledged.
Uncontrolled Keywords: permeable pavement, storm water management, neural networks, water quality models, urban drainage, geothermal heat pump
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Related URLs:
Last Modified: 12 Nov 2019 16:26
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/13103

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