Semidistributed Form of the Tank Model Coupled with Artificial Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 5
Abstract
Traditional conceptual rainfall–runoff models in the lumped form are usually developed without consideration of the spatial variation of rainfall and the heterogeneity of the watershed geomorphological nature. As an improvement to traditional conceptual models of the lumped form, a semidistributed form of the Tank model coupled with artificial neural networks (ANNs) is proposed herein. As a result, the effect of spatial variations of rainfall and model parameters can be investigated by dividing the entire catchment into a number of subcatchments and applying the spatially varied rainfall inputs and parameters to each subcatchment. Furthermore, in contrast to the linear summation commonly used in watershed routing that usually regards the total simulated runoff at the entire catchment outlet as a linear superposition of the routed runoff from all individual subcatchments, artificial neural networks are employed to explore nonlinear transformations of the runoff generated from the individual subcatchments into the total runoff at the entire watershed outlet. As illustrated in this study, coupling ANNs with traditional conceptual models reveals a promising new approach to catchment rainfall-runoff modeling.
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References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrologic Eng., 5(2), 115–123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrologic Eng., 5(2), 124–127.
Burian, S. J., Durrans, S. R., Nix, S. J., and Pitt, R. E. (2001). “Training artificial neural networks to perform rainfall disaggregation.” J. Hydrologic Eng., 6(1), 43–51.
Chandramouli, V., and Raman, H. (2001). “Multireservoir modeling with dynamic programming and neural networks.” J. Water Resour. Plan. Manage., 127(2), 89–98.
Coulibaly, P., Anctil, F., and Bobee, B. (2000). “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” J. Hydrol., 230, 244–257.
Dawson, C. W., and Wilby, R. (1998). “An artificial neural network approach to rainfall–runoff modelling.” Hydrol. Sci. J., 43(1), 47–66.
French, M. N., Krajewski, W. F., and Cuykendal, R. R. (1992). “Rainfall forecasting in space and time using a neural network.” J. Hydrol., 137, 1–37.
Funahashi, K. (1989). “On the approximate realization of continuous mappings by neural networks.” Neural Networks, 2, 183–192.
Hagan, M. T., Demuth, H. B., and Beale, M. (1996). Neural network design, PWS/Kent Publishing Co., Boston.
Holland, J. H. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, Mich.
Hornik, K., Stinchcombe, M., and White, H. (1989). “Multilayer feedforward networks are universal approximators.” Neural Networks, 2(5), 359–366.
Hsu, K.-I., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Johnson, V. M., and Rogers, L. L. (1995). “Location analysis in groundwater remediation using ANNs.” Ground Water, 33(5), 749–758.
Maier, H. R., and Dancy, G. (1996). “The use of artificial neural networks for the prediction of water quality parameters.” Water Resour. Res., 32(4), 1013–1022.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models. Part 1. A discussion of principles.” J. Hydrol., 10, 282–290.
Neelakantan, T. R., and Pundarikanthan, N. V. (2000). “Neural network-based simulation-optimization model for reservoir operation.” J. Water Resour. Plan. Manage., 126(2), 57–64.
Olsson, J., et al. (2004). “Neural networks for rainfall forecasting by atmospheric downscaling.” J. Hydrol., 9(1), 1–12.
Ranjithan, S., and Eheart, J. W. (1993). “Neural network-based screening for groundwater reclamation under uncertainty.” Water Resour. Res., 29(3), 563–574.
Roger, L. L., and Dowla, F. U. (1994). “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling.” Water Resour. Res., 30(2), 457–481.
Rosenbrock, H. H. (1960). “An automatic method for finding the greatest or least value of a function.” Comput. J., 3, 175–184.
Rzempoluck, E. J. (1998), Neural network data analysis using simulent, Springer, New York.
Sajikumar, N., and Thandaveswara, B. S. (1999). “A non-linear rainfall-runoff model using an artificial neural network.” J. Hydrol., 216, 32–55.
Shamseldin, A. Y. (1997). “Application of a neural network technique to rainfall-runoff modelling.” J. Hydrol., 199, 272–294.
Spendy, W., Hext, G. R., and Himsworth, F. R. (1962). “Sequential application of simplex design in optimisation and evolutionary design.” Technometrics, 4, 441–461.
Suen, J. P., and Eheart, J. W. (2003). “Evaluation of neural networks for modeling nitrate concentrations in rivers.” J. Water Resour. Plan. Manage., 129(6), 505–510.
Sugawara, M. (1961). “On the analysis of runoff structure about several Japanese rivers.” Jpn. J. Geophys., 2(4), 1–76.
Sugawara, M. (1967). “The flood forecasting by a series storage type model.” Int. Proc., Symp. on Floods and their Computation, Leningrad, USSR, 1–6.
Sugawara, M. (1979). “Automatic calibration of the Tank model.” Hydrol. Sci. Bull., 24(3), 375–388.
Sugawara, M. (1984). “Tank model with snow component.” Research Note of the National Research Center for Disaster Prevention, No. 65, 1–293.
Sugawara, M. (1995). “Tank model.” Computer models of watershed hydrology, V. P. Singh, ed., Water Resources Publications, Littleton, Colo.
Thirumalaiah, K., and Deo, M. C. (2000). “Hydrological forecasting using neural networks.” J. Hydrologic Eng., 5(2), 180–189.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural network.” J. Hydrologic Eng., 4(3), 232–239.
Tokar, A. S., and Markus, M. (2000). “Precipitation-runoff modeling using artificial neural networks and conceptual models.” J. Hydrologic Eng., 5(2), 156–161.
Wang, Q. J. (1991). “The genetic algorithm and its application to calibrating conceptual rainfall-runoff models.” Water Resour. Res., 27(9), 2367–2471.
Zhang, B., and Govindaraju, R. S. (2003). “Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds.” J. Hydrol., 273(1–4), 18–34.
Zhao, R. J. (1992). “The Xinanjiang model applied in China.” J. Hydrol., 135, 371–381.
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Information
Published In
Journal of Hydrologic Engineering
Volume 11 • Issue 5 • September 2006
Pages: 408 - 417
Copyright
© 2006 ASCE.
History
Received: Nov 22, 2004
Accepted: Jan 4, 2006
Published online: Sep 1, 2006
Published in print: Sep 2006
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