Abstract
We have seen in Chap. 1 that, in the derivation of advection–dispersion equation, also known as continuum transport model (Rashidi et al. 1999), the velocity fluctuations around the mean velocity enter into the calculation of solute flux at a given point through averaging theorems. The mean advective flux and the mean dispersive flux are then related to the concentration gradients through Fickian–type assumptions. These assumptions are instrumental in defining dispersivity as a measure of solute dispersion. Dispersivity is proven to be scale dependant.
References
Abrahart RJ, Linda S, Pauline EK (1999) Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model. J Hydroinformatics 1(2):103–114
Basawa IV, Prakasa Rao BLS (1980) Statistical inference for stochastic processes. Academic Press, New York
Bebis G, Georgiopoulos M (1994) Feed-forward neural networks: why network size is so important. IEEE Potentials, 27–31 Oct/Nov 1994
Castellano G, Fanelli AM, Pelillo M (1997) An iterative pruning algorithm for feed-forward neural networks. IEEE Trans Neural Netw 5:961–970
Chakilam VM (1998) Forecasting the future: experimenting with time series data. Unpublished master’s thesis, Birla Institute of Technology and Science, India. Accessed 8 Sept 2001
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:203–314
Fetter CW (1999) Contaminant hydrogeology. Prentice-Hall, New Jersey
Flood I, Kartam N (1994) Neural network in civil engineering. I: Principles and understanding. J Comput Civil Eng 8(2):131–148
Gaines JG, Lyons TJ (1997) Variable step size control in the numerical solution of stochastic differential equations. SIAM J Appl Math 57:1455–1484
Gelhar LW (1986) Stochastic subsurface hydrology from theory to applications. Water Resour Res 22(9):1355–1455
Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: 1st IEEE international joint conference on neural networks. Institute of Electrical and Electronic Engineering, San Diego, pp 11–14, 21–24 June 1987
Hernandez DB (1995) Lectures on Probability and Second Order Random Fields. World Scientific, Singapore
Holden H, Øksendal B, Uboe J, Zhang T (1996) Stochastic partial differential equations. Birkhauser, Boston
Jazwinski AH (1970) Stochastic processes and filtering theory. Academic Press, New York
Klebaner FC (1998) Introduction to stochastic calculus with applications. Springer-Verlag, New York
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480
Kulasiri D, Verwoerd W (1999) A stochastic model for solute transport in porous media: mathematical basis and computational solution. Proc MODSIM 1999 Inter Congr Model Simul 1:31–36
Kulasiri D, Verwoerd W (2002) Stochastic Dynamics: Modeling Solute Transport in Porous Media, North-Holland Series in Applied Mathematics and Mechanics, vol 44. Elsevier Science Ltd., Amsterdam
Kutoyants YuA (1984) Parameter estimation for stochastic processes. Herderman Verlag, Berlin
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw. 15:101–124
Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrol Sci J 41(3):399–417
Morton KW, Mayers DF (1994) Numerical solution of partial differential equations. Cambridge University Press, Cambridge
Pickens JF, Grisak GE (1981) Scale-dependent dispersion in a stratified granular aquifer. Water Resour Res 17(4):1191–1211
Sarle WS (1994) Neural networks and statistical models. In: Proceedings of 19th annual SAS users group international conference, pp 1538–1550
Towell GG, Craven MK, Shavlik JW (1991) Constructive induction in knowledge-based neural networks. In: Proceedings of the 8th International Workshop on Machine Learning, Morgan Kaufman, San Mateo, pp 213–217
Unny TE (1985) Stochastic partial differential equations in groundwater hydrology. Part 1. Stoch Hydrol Hydraul 3:135–153
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Kulasiri, D. (2013). A Stochastic Model for Hydrodynamic Dispersion. In: Non-fickian Solute Transport in Porous Media. Advances in Geophysical and Environmental Mechanics and Mathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34985-0_3
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