ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff
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VIEW THE REPLYPublication: Journal of Hydraulic Engineering
Volume 132, Issue 12
Abstract
This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.
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References
Anctil, F., and Rat, A. (2005). “Evaluation of neural network streamflow forecasting on 47 watersheds.” J. Hydrol. Eng., 10(1), 85–88.
ASCE Task Committee. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
ASCE Task Committee. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrol. Eng., 5(2), 124–137.
Barfield, B. J., Barnhisel, R. I., Powell, J. L., Hirschi, M. C., and Moore, I. D. (1983). “Erodibilities and eroded size distribution of western Kentucky mine spoil and reconstructed topsoil.” Institute for Mining and Minerals Research Final Rep., Univ. of Kentucky, Lexigton, Ky.
Chang, L. C., Chang, F. J., and Tsai, Y. H. (2005). “Fuzzy exemplar-based inference system for flood forecasting.” Water Resour. Res., 41.
Dawson, W. C., and Wilby, R. (1998). “An artificial neural network approach to rainfall-runoff modeling.” Hydrol. Sci. J., 43(1), 47–66.
Govindaraju, R. S., Kavvas, M. L., and Tayfur, G. (1992). “A simplified model for two dimensional overland flows.” Adv. Water Resour., 15, 133–141.
Hong, Y. S., Rosen, M. R., and Reeves, R. R. (2002). “Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers.” J. Hydrol. Eng., 7(5), 380–391.
Jantzen, J. (1999). “Design of fuzzy controllers.” Technical Rep. No. 98-E864, Dept. of Automation, Technical Univ. of Denmark, Denmark.
Kilinc, M., and Richardson, E. V. (1973). “Mechanics of soil erosion from overland flow generated by simulated rainfall.” Hydrology papers, Colorado State Univ., Fort Collins, Colo., Paper 63.
Maskey, S., Guinot, V., and Price, R. K. (2004). “Treatment of precipitation uncertainty in rainfall-runoff modeling: A fuzzy set approach.” Adv. Water Resour., 27(9), 889–898.
McNeill, F. M., and Thro, E. (1994). Fuzzy logic: A practical approach, Hyperion, New York.
Olsson, J., et al. (2004). “Neural networks for rainfall forecasting by atmospheric downscaling.” J. Hydrol. Eng., 9,1 1–12.
Ozelkan, E. C., and Duckstein, L. (2001). “Fuzzy conceptual rainfall-runoff models.” J. Hydrol., 253(1–4), 41–68.
Rajurkar, M. P., Kothyari, U. C., and Chaube, U. C. (2002). “Artificial neural networks for daily rainfall-runoff modeling.” Hydrol. Sci. J., 47(6), 865–877.
Ramírez, M. C. V., Velho, H. F. C., and Ferreira, N. J. (2005). “Artificial neural network technique for rainfall forecasting applied to the São Paulo region.” J. Hydrol., 301(1–4), 146–162.
See, L., and Openshaw, S. (2000). “A hybrid multi-model approach to river level forecasting.” Hydrol. Sci. J., 45(4), 523–536.
Sen, Z. (1998). “Fuzzy algorithm for estimation of solar irradiation from sunshine duration.” Sol. Eng., 63(1), 39–49.
Sen, Z. (1999). “Fuzzy modelling in engineering.” Class notes, Civil Engineering Faculty, Istanbul Technical Univ., Istanbul, Turkey (in Turkish).
Somez, I. (1998). “Meteorological applications of artificial neural networks.” MSc thesis, Dept. of Meteorological Engineering, Istanbul Technical Univ., Istanbul, Turkey (in Turkish).
Tayfur, G. (2002a). “Applicability of sediment transport capacity models for non-steady state erosion from steep slopes.” J. Hydrol. Eng., 7(3), 252–259.
Tayfur, G. (2002b). “Artificial neural networks for sheet sediment transport.” Hydrol. Sci. J., 47(6), 879–892.
Tayfur, G., Kavvas, M. L., Govindaraju, G. S., and Storm, D. E. (1993). “Applicability of St. Venant equations for two-dimensional overland flows over rough infiltrating surfaces.” J. Hydraul. Eng., 119(1), 51–63.
Tayfur, G., Ozdemir, S., and Singh, V. P. (2003). “Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces.” Adv. Water Resour., 26, 1249–1256.
Tayfur, G., and Singh, V. P. (2005). “Predicting longitudinal dispersion coefficient in natural streams by artificial neural network.” J. Hydraul. Eng., 131(11), 991–1000.
Tayfur, G., Swiatek, D., Wita, A., and Singh, V. P. (2005). “Case study: Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland.” J. Hydraul. Eng., 131(6), 431–440.
Tilmant, A., Vanclooster, M., Duckstein, L., and Persoons, E. (2002). “Comparison of fuzzy and nonfuzzy optimal reservoir operating policies.” J. Water Resour. Plann. Manage., 128(6), 390–398.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrol. Eng., 4(3), 232–239.
Tokar, S. A., and Markus, M. (2000). “Precipitation-runoff modeling using artificial neural networks and conceptual models.” J. Hydrol. Eng., 5(2), 156–161.
Woolhiser, D. A. (1974). “Unsteady free-surface flow problems.” Proc., Inst. on Unsteady Flow in Open Channels, Colorado State Univ., Fort Collins, Colo., 195–213.
Wu, J. S., Han, J., Annambhotla, S., and Bryant, S. (2005). “Artificial neural networks for forecasting watershed runoff and stream flows.” J. Hydrol. Eng., 10(3), 216–222.
Yu., P.-S., and Yang, T.-C. (2000). “Fuzzy multi-objective function for rainfall-runoff model calibration.” J. Hydrol., 238(1–2), 1–14.
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Information
Published In
Journal of Hydraulic Engineering
Volume 132 • Issue 12 • December 2006
Pages: 1321 - 1330
Copyright
© 2006 ASCE.
History
Received: Oct 19, 2004
Accepted: Feb 7, 2006
Published online: Dec 1, 2006
Published in print: Dec 2006
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