Predicting Residence Time of Pollutants in Transient Storage Zones of Rivers by Genetic Programming
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Predicting Residence Time of Pollutants in Transient Storage Zones of Rivers by Genetic Programming

Authors: Rajeev R. Sahay

Abstract:

Rivers have transient storage or dead zones where injected pollutants or solutes are entrapped for considerable period of time, known as residence time, before being released into the main flowing zones of rivers. In this study, a new empirical expression for residence time, implementing genetic programming on published dispersion data, has been derived. The proposed expression uses few hydraulic and geometric characteristics of rivers which are normally known to the authorities. When compared with some reported expressions, based on various statistical indices, it can be concluded that the proposed expression predicts the residence time of pollutants in natural rivers more accurately.

Keywords: Parameter estimation, pollutant transport, residence time, rivers, transient storage.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110499

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[1] K. E. Bencala and R. A. Walters, “Simulation of solute transport in a mountain pool-and-riffle stream”, Water Resources Research, Vol. 19, pp. 718-724, 1983.
[2] F. B. Pedersen, Prediction of longitudinal dispersion in natural streams. Series Paper 14, Technical University of Denmark, Lyngby, 1977.
[3] C. F. Nordin and G. V. Sabol, Empirical data on longitudinal dispersion. US Geol Survey Water Resour Invest Report, 1974, pp. 20– 74.
[4] A. Okubo, “Effect of shoreline irregularities on stream wise dispersion in estuaries and other embayments”, Netherlands J of Sea Research, Vol. 6, pp. 213–224, 1973.
[5] T. S. Cheong and I. W. Seo, “Parameter estimation of the transient storage model by a routing method for river mixing processes”, Water Resources Research, Vol. 39, pp.1074–1084, 2003.
[6] T. S. Cheong, B. A. Younis and I. W. Seo, “Estimation of key parameters in model for solute transport in rivers and streams”, Water Resources Management, Vol. 21, pp. 1165–1186, 2007.
[7] P. M. Rowiński and A. Piotrowski, “Estimation of parameters of the transient storage model by means of multi-layer perceptron neural networks”, Hydrological Sciences Journal, Vol. 53, pp. 165-178, 2008.
[8] Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In J. J. Grefenstette, ed., Proceedings of the first international conference on genetic algorithms and their applications Erlbaum 183-187.
[9] J. R. Koza, Genetic Programming: On the programming of computers by means of natural selection. Cambridge, MIT Press, 1992.
[10] D. P. Searson, D. E. Leahy and M. J. Willis, “GPTIPS: An open source genetic programming toolbox for multigene symbolic regression”, in Proc. Inter. Multi-conference of Engineers and Computer Scientists, Hong Kong. 2010.