Self-organizing maps with multiple input-output option for modeling the Richards equation and its inverse solution

Water Resources Research

Volumn 41, Issue 3, 2005, Pages 1-10

  Schutze N ^a   Schmitz, G H ^a   Petersohn, U ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATA ACQUISITION; HYDRAULICS; MATHEMATICAL MODELS; MONTE CARLO METHODS; NEURAL NETWORKS; OPTIMIZATION;

IRRIGATION PARAMETERS; RICHARDS EQUATION; SOIL HYDRAULIC PARAMETERS; STOCHASTIC SIMULATION;

SELF ORGANIZING MAPS;

HYDRAULIC PROPERTY; INVERSE PROBLEM; RICHARDS EQUATION; SOIL WATER;

EID: 17844388891     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2004WR003630     Document Type: Article

References (37)

1. American Society of Civil Engineers (ASCE) (2000a), Artificial neural networks in hydrology. I: Preliminary concepts, J. Hydrol. Eng., 5, 115-123.
2. American Society of Civil Engineers (ASCE) (2000b), Artificial neural networks in hydrology. II: Hydrologic applications, J. Hydrol. Eng., 5, 124-137.
3. Atiya, A. F., and S. I. Shaheen (1999), A comparison between neural-network forecasting techniques case study: River flow forecasting, IEEE Trans. Neural Networks, 18, 402-409.
4. Barber, C. B., D. Dobkin, and H. Huhdanpaa (1996), The Quickhull algorithm for convex hulls, ACM Trans. Math. Software, 22, 469-483.
5. Bowden, G. H., H. R. Maier, and G. C. Dandy (2002), Optimal division of data for neural network models in water resources applications, Water Resour. Res., 38(2), 1010, doi:10.1029/2001WR000266.
6. Cai, S., H. Toral, J. Qiu, and J. Archer (1994), Neural network based objective flow regime identification in air-water two phase flow, Can. J. Chem. Eng., 72, 440-445.
7. Dawson, C. W., and R. L. Wilby (1999), A comparison of artificial neural networks used for river flow forecasting, Hydrol. Earth Syst. Sci., 3, 529-540.
8. Dawson, C. W., and R. L. Wilby (2001), Hydrological modelling using artificial neural networks, Prog. Phys. Geogr., 25, 80-108.
9. Felgenhauer, A., F. Lennartz, and G. H. Schmitz (1999), On the uniqueness of the parameters of commonly used soil models, Geophys. Res. Abstr., 1, 304.
10. Haverkamp, R. (1983), Resolution de l'équation de l'infiltration de l'eau dans le sol: Aproches analytiques et numériques, Ph.D. thesis, Univ. Sci. Technol. et Médicale, Grenoble, France.
11. Healy, R. W., and A. D. Ronan (2000), Documentation of computer program VS2DH for simulation of energy transport in variably saturated porous media - Modification of the U.S. Geological Survey's computer program VS2DT, U.S. Geol. Surv. Water Resour. Invest., 96-4230.
12. Hecht-Nielsen, R. (1987), Counterpropagation networks, in Proceedings of the International Conference on Neural Networks, vol. 3, pp. 11-13, IEEE Press, Piscataway, N. J.
13. Hsu, K. L., H. V. Gupta, and S. Sorooshian (1995), Artificial neural network modeling of the rainfall-runoff process, Water Resour. Res., 31, 2517-2530.
14. Hsu, K.-L., X. Gao, S. Sorooshian, and H. V. Gupta (1997), Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36, 1176-1190.
15. Hsu, K.-L., H. V. Gupta, X. Gao, S. Sorooshian, and B. Imam (2002), Self-organizing linear output map (SOLO); An artificial neural network suitable for hydrologic modeling and analysis, Water Resour. Res., 38(12), 1302, doi:10.1029/2001WR000795.
16. Jayawardena, A. W., and T. M. Fernando (2001), River flow prediction: An artificial neural network approach, IAHS Publ., 268, 239-246.
17. Kohonen, T. (2001), Self-Organizing Maps, Springer, New York.
18. Kothari, R., and S. Islam (1999), Spatial characterization of remotely sensed soil moisture data using self organizing feature maps, IEEE Trans. Geosci. Remote Sens., 37, 1162-1165.
19. Kumar, A., and K. V. Minocha (2001), Discussion of "Rainfall runoff modeling using artificial neural networks" by A. S. Tokar and P. A. Johnson, J. Hydrol. Eng., 6, 176-177.
20. Maier, H. R., and C. D. Dandy (2000), Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications, Environ. Modell. Software, 15, 101-124.
21. Mallet, J. L. (2002), Geomodeling, Oxford Univ. Press, New York.
22. Meshkat, M., R. C. Warner, and S. R. Workman (1999), Modeling of evaporation reduction in drip irrigation system, J. Irrig. Drainage Eng., 125, 315-323.
23. Minns, A. W., and M. J. Hall (1996), Artificial neural networks as rainfall-runoff models, Hydrol. Sci., 41, 399-417.
24. Pan, L., and L. Wu (1998), A hybrid global optimization method for inverse estimation of hydraulic parameters: Annealing-simplex method, Water Resour. Res., 34, 2261-2270.
25. Philip, J. R. (1957), The theory of infiltration: IV sorptivity and algebraic infiltration equations, J. Soil Sci., 84, 257-265.
26. Ranayake, C., S. Samarasinghe, and D. Kulasiri (2002), Solving the inverse problem in stochastic groundwater modelling with artificial neural networks, in Biennial Congress of the International Environmental Modelling and Software Society, vol. 2, pp. 154-159, IEMSS, Lugano, Switzerland.
27. Ritter, H., T. Martinetz, and K. Schulten (1992), Neural Computation and Self-Organizing Maps: An Introduction, Addison-Wesley, Boston, Mass.
28. Rizzo, D. M., and D. E. Dougherty (1994), Characterization of aquifer properties using artificial neural networks: Neural kriging, Water Resour. Res., 30, 483-497.
29. Schmitz, G. H., and R. Liedl (1998), Hydrodynamic-analytical modeling for irrigation analysis, Arabian J. Sci. Eng., 23, 43-66.
30. Schmitz, G. H., and N. Schütze (2002), A new strategy for optimizing water application under trickle irrigation, J. Irrig. Drainage Eng., 128, 287-297.
31. Schütze, N., and G. H. Schmitz (2003), Optimizing irrigation efficiency with artificial neural networks, paper presented at the Information Technologies in Environmental Engineering Meeting, Gdansk Univ. of Technol., Poland, 24-27 June.
32. Sezin, T. A., and P. A. Johnson (1999), Rainfall-runoff modeling using artificial neural networks, J. Hydraul. Eng., 4, 232-239.
33. Shamseldin, A. Y. (1997), Application of a neural network technique to rainfall-runoff modelling, J. Hydrol., 199, 272-294.
34. Shamseldin, A. Y., and K. M. O'Connor (2001), A non-linear neural network technique for updating of river flow forecasts, Hydrol. Earth Syst. Sci., 5, 577-597.
35. Simúnek, J., M. Šejna, and M. T. van Genuchten (1996), Hydrus-2D: User manual, Rep. IGWMC-TPS 53, Int. Ground Water Model, Cent. Colo. School of Mines, Golden.
36. Smith, J., and R. N. Eli (1995), Neural network models of rainfall-runoff process, J. Water Resour. Plann. Manage., 121, 499-508.
37. Vogel, T., M. T. van Genuchten, and M. Cislerova (2000), Effect of the shape of the soil hydraulic functions near saturation on variably-saturated flow predictions, Adv. Water Resour. Res., 24, 133-144.