River flow prediction using an integrated approach

Journal of Hydrologic Engineering

Volumn 14, Issue 1, 2009, Pages 75-83

  Srinivasulu, Sanaga ^a   Jain, Ashu ^b  

^a JNTUH COLLEGE OF ENGINEERING   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

Author keywords

Algorithms; Forecasting; Hybrid methods; Hydrology; Predictions; Rainfall; River flow; Runoff; Water resources

Indexed keywords

BACKPROPAGATION; COMPUTER NETWORKS; FLOW OF WATER; GENETIC ALGORITHMS; HYBRID SENSORS; HYDROLOGY; INTEGRATED CONTROL; MANAGEMENT; NEURAL NETWORKS; RAIN; RESOURCE ALLOCATION; RIVERS; RUNOFF; STREAM FLOW; STRESS INTENSITY FACTORS; WATER; WATER MANAGEMENT; WATER RESOURCES;

AND MODELS; AREA METHODS; ARTIFICIAL NEURAL NETWORKS; AUTOREGRESSIVE; CONCEPTUAL MODELS; FLOOD ROUTING; FORECAST ACCURACIES; FUSION TECHNIQUES; HYBRID METHODS; HYBRID MODELS; INTEGRATED APPROACHES; INTEGRATED MODELS; KENTUCKY; MODEL PERFORMANCES; MODELING AND FORECASTING; PREDICTIONS; RAINFALL; REASONABLE ACCURACIES; RESOURCE MANAGEMENTS; RIVER BASINS; RIVER FLOW; RIVER FLOW FORECASTING; RIVER FLOW PREDICTIONS; STANDARD PERFORMANCES; TIME SERIES;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; GENETIC ALGORITHM; INTEGRATED APPROACH; PREDICTION; RAINFALL-RUNOFF MODELING; RIVER FLOW;

EID: 57949116748     PISSN: 10840699     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)1084-0699(2009)14:1(75)     Document Type: Article

References (35)

1. Anctil, F., and Tape, D. G. (2004). "An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition." J. Environ. Eng. Sci. JEESAX1496-2551, 3(1), S121-S128.
2. Birikundavyi, S., Labib, R., Trung, H. T., and Rousselle, J. (2002). "Performance of neural networks in daily streamflow forecasting." J. Hydrol. Eng. JHYEFF1084-069910.1061/ (ASCE)1084-0699(2002)7:5(392), 7(5), 392-398.
3. Campolo, M., Andreussi, P., and Soldati, A. (1999). "River flood forecasting with neural network model." Water Resour. Res. WRERAQ0043-139710.1029/1998WR900086, 35(4), 1191-1197.
4. Chen, J., and Adams, B. J. (2005). "Integration of artificial neural networks with conceptual models in rainfall-runoff modeling." J. Hydrol. JHYDA70022-169410.1016/j.jhydrol.2005.06.017, 318(1-4), 232-249.
5. Chow, V. T., Maidment, D. R., and Mays, L. W. (1988). Applied hydrology, McGraw-Hill, New York.
6. Curry, B., and Morgan, P. (1997). "Neural network: A need for caution." Omega-Int. J. Manage. Sci., 25(1), 123-133.
7. Dawson, D. W., and Wilby, R. (1998). "An artificial neural network approach to rainfall-runoff modeling." Hydrol. Sci. J. HSJODN0262-6667, 43(1), 47-65.
8. Deb, K. (2001). "An efficient constraint handling method for genetic algorithms." Comput. Methods Appl. Mech. Eng.CMMECC0045-782510.1016/S0045-7825(99)00389-8, 186, 311-338.
9. Deb, K., and Agarwal, R. B. (1995). "Simulated binary crossover for continuous search space." Complex Syst. CPSYEN0891-2513, 9, 115-148.
10. Deb, K., and Goyal, M. (1996). "A combined genetic adaptive search (GeneAS) for engineering design." Comput. Sci. Inform. CSINET0254-7813, 26(4), 30-45.
11. Haan, C. T. (1972). "A water yield model for small watersheds." Water Resour. Res. WRERAQ0043-1397, 8(1), 58-69.
12. Hsu, K.-L., Gupta, H. V., Gao, X., Sorooshian, S., and Imam, B. (2002). "Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis." Water Resour. Res. WRERAQ0043-139710.1029/2001WR000795, 38(12), 1302 10.1029/ 2001WR000795.
13. Hsu, K.-L., Gupta, H. V., and Sorooshian, S. (1995). "Artificial neural network modeling of the rainfall-runoff process." Water Resour. Res. WRERAQ0043-139710.1029/95WR01955, 31(10), 2517-2530.
14. Hydrologic Engineering Center. (1990). HEC-1 flood hydrograph package user's manual and programmer's manual, U.S. Army Corps of Engineers, Davis, Calif.
15. Jain, A., and Indurthy, S. K. V. P. (2003). "Comparative analysis of event based rainfall-runoff modeling techniques-Deterministic, statistical, and artificial neural networks." J. Hydrol. Eng. JHYEFF1084-069910.1061/(ASCE)1084-0699(2003)8:1(1), 8(2), 1-6.
16. Jain, A., and Kumar, A. (2006). "An evaluation of artificial neural network technique for the determination of infiltration model parameters." J. Appl. Soft Comput., 6(3), 272-282.
17. Jain, A., and Kumar, A. M. (2007). "Hybrid neural network models for hydrologic time series forecasting." J. Appl. Soft Comput., 7(2), 585-592.
18. Jain, A., and Ormsbee, L. E. (2002). "Evaluation of short-term water demand forecast modeling techniques: Conventional methods versus AI." J. Am. Water Works Assoc. JAWWA50003-150X, 94(7), 64-72.
19. Jain, A., and Ormsbee, L. E. (2004). "An evaluation of the available techniques for estimating missing fecal coliform data." J. Am. Water Resour. Assoc. JWRAF51093-474X, 40(6), 1617-1630.
20. Jain, A., and Srinivasulu, S. (2004). "Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded GA, and ANN techniques." Water Resour. Res. WRERAQ0043-139710.1029/2003WR002355, 40(4), W04302.10.1029/2003WR002355
21. Jain, A., and Srinivasulu, S. (2006). "Integrated approach to modelling decomposed flow hydrograph using artificial neural network and conceptual techniques." J. Hydrol. JHYDA70022-1694, 317(3-4), 291-306.
22. Jain, A., Varshney, A. K., and Joshi, U. C. (2001). "Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks." Water Resour. Manage. WRMAEJ0920-474110.1023/ A:1014415503476, 15(5), 299-321.
23. Kumar, J., Jain, A., and Srivastava, R. (2006). "Neural network based solutions for locating groundwater pollution sources." Hydrol. J., 29(1-2), 55-66.
24. Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resource variables: A review of modeling issues and applications." Environ. Modell. Software EMSOFT1364-815210.1016/S1364-8152(99)00007-9, 15, 101-124.
25. Minns, A. W., and Hall, M. J. (1996). "Artificial neural networks as rainfall runoff models." Hydrol. Sci. J. HSJODN0262-6667, 41(3), 399-417.
26. Mizumara, K. (1995). "Runoff prediction by simple tank model using recession curves." J. Hydraul. Eng. JHEND80733-942910.1061/ (ASCE)0733-9429(1995)121:11(812), 121(11), 812-817.
27. Porporato, A., and Ridolfi, L. (2001). "Multivariate nonlinear prediction of river flows." J. Hydrol. JHYDA70022-1694, 248, 109-122.
28. Raman, H., and Sunilkumar, N. (1995). "Multivariate modeling of water resources time series using artificial neural networks." Hydrol. Sci. J. HSJODN0262-6667, 40(2), 145-163.
29. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). "Learning representations by back-propagating errors." Nature NATUAS0028-083610.1038/323533a0, 323, 533-536.
30. Sajikumar, N., and Thandaveswara, B. S. (1999). "A non-linear rainfall-runoff model using an artificial neural network." J. Hydrol. JHYDA70022-169410.1016/S0022-1694(98)00273-X, 216, 32-55.
31. Shamseldin, A. Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." J. Hydrol. JHYDA70022-169410.1016/S0022-1694(96)03330-6, 199, 272-294.
32. Smith, J., and Eli, R. N. (1995). "Neural network models of the rainfall runoff process." J. Water Resour. Plann. Manage. JWRMD50733-949610.1061/(ASCE)0733-9496(1995)121:6(499), 121(6), 499-508.
33. Srinivasulu, S., and Jain, A. (2006). "A comparative analysis of training methods for artificial neural network rainfall-runoff modeling." J. Appl. Soft Comput., 6(3), 295-306.
34. Tokar, A. S., and Markus, M. (2000). "Precipitation runoff modeling using artificial neural network and conceptual models." J. Hydrol. Eng. JHYEFF1084-069910.1061/(ASCE)1084-0699(2000)5:2(156), 5(2), 156-161.
35. Zhang, B., and Govindaraju, S. (2000). "Prediction of watershed runoff using bayesian concepts and modular neural networks." Water Resour. Res. WRERAQ0043-139710.1029/1999WR900264, 36(3), 753-762.