Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents;Evaluation des profils temporels de concentration de cours d'eau non jaugés à la suite d'incidents de pollution

Hydrological Sciences Journal

Volumn 56, Issue 5, 2011, Pages 883-894

  Piotrowski, Adam P ^a   Napiorkowski, Jaroslaw J ^a   Rowinski, Pawel M ^a   Wallis, Steve G ^b  

^a INSTITUTE OF GEOPHYSICS   (Poland)

^b HERIOT WATT UNIVERSITY   (United Kingdom)

Author keywords

data based modelling; longitudinal dispersion; neural networks; pollutant transport; ungauged river

Indexed keywords

DATA-BASED MODELLING; DISPERSION COEFFICIENT; LONGITUDINAL DISPERSIONS; OPTIMIZED PARAMETER; PARAMETER ESTIMATE; PHYSICAL VARIABLES; POLLUTANT TRANSPORT; POLLUTION INCIDENTS; QUALITY OF PREDICTIONS; SECOND-ORDER POLYNOMIAL; TEMPORAL CONCENTRATIONS; TRACER EXPERIMENT; UNGAUGED RIVERS; VELOCITY PREDICTION;

FORECASTING; NEURAL NETWORKS; PARAMETER ESTIMATION; POLLUTION; RIVERS;

RIVER POLLUTION;

ARTIFICIAL NEURAL NETWORK; CONCENTRATION (COMPOSITION); DATABASE; DISPERSION; FLOW VELOCITY; MORPHOMETRY; POLLUTANT TRANSPORT; RIVER DISCHARGE; RIVER POLLUTION;

EID: 80052024692     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626667.2011.583398     Document Type: Article

References (37)

1. Bencala, K.E. and Walters, R.A. 1983. Simulation of solute transport in a mountain pool-and-riffle stream: a transient storage model. Water Resour. Res., 19(3): 718-724.
2. Burke, N.J. 2002. Travel time and flow characteristics of a small stream system. PhD Thesis, Heriot-Watt University, Edinburgh, UK: unpublished.
3. Cigizoglu, H.K. 2003. Estimation, forecasting and extrapolation of flow data by artificial neural networks. Hydrol. Sci. J., 48(3): 349-361.
4. Czernuszenko, W. and Rowinski, P.M. 1997. Properties of the dead-zone model of longitudinal dispersion in rivers. J.Hydraul. Res., 35(4): 491-504.
5. Das, S., Abraham, A., Chakraborty, U.K. and Konar, A. 2009. Differential Evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput., 13(3): 526-553.
6. Durand, M., Rodriguez, E., Alsdorf, D.E. and Trigg, M. 2010. Estimating river depth from remote sensing swath interferometry measurements of river height, slope and width. IEEE J Sel. Top. Appl., 3(1): 20-31.
7. Godfrey, R.G. and Frederick, B.J. 1970. Stream dispersion at selected sites, Washington, DC: US Geol. Survey Prof. Paper 433-K.
8. Haykin, S. 1999. Neural Networks: A Comprehensive Foundation, New York: Macmillan.
9. Jain, S.K., Das, D. and Srivastava, D.K. 1999. Application of ANN for reservoir inflow prediction and operation. J. Water Resour. Plan. Manage. ASCE, 125(5): 263-271.
10. Kashefipour, S.M., Falconer, R.A. and Lin, B. 2002. "Modeling longitudinal dispersion in natural channel flows using ANNs". In River Flow 2002, Edited by: Bousmar, D. and Zech, Y. 111-116. Lisse: Balkema/Swets.
11. Maier, H.R. and Dandy, G.C. 2000. Neural network for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Model. Software, 15: 101-124.
12. Nash, J. E. and Sutcliffe, J.V. 1970. River flow forecasting through conceptual models, Part I--A discussion of principles. J. Hydrol., 10(3): 282-290.
13. Nordin, C.F. and Sabol, G.V. 1974. Empirical data on longitudinal dispersion in rivers, 20-74. Washington, DC: US Geol. Survey Water Resour. Invest. Report.
14. Nordin, C.F. and Troutman, B.M. 1980. Longitudinal dispersion in rivers: the presence of skewness in observed data. Water Resour. Res., 16(1): 123-128.
15. Partal, T. and Cigizoglu, H.K. 2009. Prediction of daily precipitation using wavelet-neural networks. Hydrol. Sci. J., 54(2): 234-246.
16. Piotrowski, A., Rowinski, P.M. and Napiorkowski, J.J. Assessment of longitudinal dispersion coefficient by means of different neural networks. Proceedings of the Seventh Int. Conf. on Hydroinformatics HIC 2006. Nice, France.
17. Piotrowski, A., Wallis, S.G., Napiorkowski, J.J. and Rowinski, P.M. 2007. Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks. Hydrol. Earth Syst. Sci., 11: 1883-1896.
18. Piotrowski, A.P. and Napiorkowski, J.J. 2010. The grouping differentia evolution algorithm for multi-dimensional optimization problems. Control Cybern., 39(2): 527-550.
19. Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. 1992. Numerical Recipes in Fortran 77: The Art of Scientific Computing, New York: Cambridge University Press.
20. Price, K.V., Storn, R.M. and Lampinen, J.A. 2005. Differential Evolution. A Practical Approach to Global Optimization, Berlin: Springer-Verlag.
21. Rezaei, K., Guest, B., Friedrich, A., Fayazi, F., Nakhaei, M., Beitollahi, A. and Aghda, S.M.F. 2009. Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran. Acta Geophys., 57(2): 271-293.
22. Rowinski, P.M., Dysarz, T. and Napiorkowski, J.J. 2004. "Estimation of longitudinal dispersion and storage zone parameters". In River Flow 2004, Edited by: Greco, M., Carravetta, A. and Della Morte, R. 1201-1210. London: Taylor & Francis Group.
23. Rowinski, P.M., Piotrowski, A. and Napiorkowski, J.J. 2005. Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers?. Hydrol. Sci. J., 50(1): 175-187.
24. Rowinski, P.M. and Piotrowski, A. 2008. Estimation of parameters of transient storage model by means of multi-layer perceptron neural networks. Hydrol. Sci. J., 53(1): 165-178.
25. Rowinski, P.M., Guymer, I. and Kwiatkowski, K. 2008. Response to the slug injection of a tracer-large-scale experiment in a natural river. Hydrol. Sci. J., 53(6): 1300-1309.
26. Rowinski, P.M. and Chrzanowski, M.M. 2011. Influence of selected fluorescence dyes on small aquatic organisms. Acta Geophysica, 59(1): 91-109.
27. Rutherford, J.C. 1994. River Mixing, Chichester: Wiley.
28. Smith, L.C., Isacks, B.L., Bloom, A.L. and Murray, A.B. 1996. Estimation of discharge from three braided rivers using synthetic aperture radar satellite imagery: Potential application to ungaged basins. Water Resour. Res., 32(7): 2021-2034.
29. Storn, R. and Price, K.V. 1995. Differential Evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, Berkeley, CA: International Computer Sciences Institute. Tech. Report TR-95-012.
30. Sukhodolov, A.N., Nikora, V.I., Rowiński, P.M. and Czernuszenko, W. 1997. A case study of longitudinal dispersion in small lowland rivers. Water Environ. Res., 69(7): 1246-1253.
31. Tang, Q.H., Gao, H.L., Lu, H. and Lettenmaier, D.P. 2009. Remote sensing: hydrology. Progr. Phys. Geogr., 33(4): 490-509.
32. Tayfur, G. 2006. Fuzzy, ANN and regression models to predict longitudinal dispersion coefficient in natural streams. Nordic Hydrol., 37(2): 143-164.
33. Toprak, Z.F. and Cigizoglu, H.K. 2008. Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods. Hydrol. Processes, 22(20): 4106-4129.
34. Wallis, S.G. and Manson, J.R. 2004. Methods for predicting dispersion coefficients in rivers. Proc. Inst. Civ. Eng., Water Manage., 157(WM3): 131-141.
35. Wallis, S.G., Piotrowski, A., Rowinski, P.M. and Napiorkowski, J.J. 2007. "Prediction of dispersion coefficients in a small stream using Artificial Neural Networks". In Proceedings of the 32nd IAHR Congress, Venice: Italy. paper B2b-083-O
36. Weber, M., Neri, F. and Tirronen, V. 2009. Distributed differential evolution with explorative-exploitative population families. Genetic Program. Evol. Model., 10: 343-371.
37. Worman, A. 2000. Comparison of models for transient storage of solutes in small streams. Water Resour. Res., 36(2): 455-468.