Comparison between response surface models and artificial neural networks in hydrologic forecasting

Journal of Hydrologic Engineering

Volumn 19, Issue 3, 2014, Pages 473-481

  Yu, Jianjun ^a,b   Qin, Xiaosheng ^a,c   Larsen, Ole ^b   Chua, L H C ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Artificial neural networks; Hydrologic forecasting; Regression; Response surface model

Indexed keywords

EID: 84894105439     PISSN: 10840699     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)HE.1943-5584.0000827     Document Type: Article

References (41)

1. Abrahart, R. J., and See, L. (2000). "Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments." Hydrol. Process., 14(11-12), 2157-2175.
2. Adeloye, A. J. (2009). "Multiple linear regression and artificial neural networks models for generalized reservoir storage-yield-reliability function for reservoir planning." J. Hydrol. Eng., 10.1061/(ASCE) HE.1943-5584.0000041, 731-738.
3. Andersson, M., and Adlercreutz, P. (1999). "Evaluation of simple enzyme kinetics by response surface modelling." Biotechnol. Tech., 13(12), 903-907.
4. Carlson, R., MacCormick, A., and Watts, D. (1970). "Application of linear random models to four annual streamflow series." Water Resour. Res., 6(4), 1070-1078.
5. Castellano-Méndez, M., González-Manteiga, W., Febrero-Bande, M., Prada-Sánchez, J. M., and Lozano-Calderón, R. (2004). "Modelling of the monthly and daily behaviour of the runoffof the Xallas River using Box-Jenkins and neural networks methods." J. Hydrol., 296(1-4), 38-58.
6. Croley, T. E., II (1980). "Gamma synthetic hydrographs." J. Hydrol., 47(1-2), 41-52.
7. Elshorbagy, A., Simonovicb, S. P., and Panu, U. S. (2002). "Estimation of missing streamflow data using principles of chaos theory." J. Hydrol., 255(1-4), 123-133.
8. Govindaraju, R. S. (2000a). "Artificial neural networks in hydrology. I: Preliminary concepts." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699 (2000)5:2(115), 115-123.
9. Govindaraju, R. S. (2000b). "Artificial neural networks in hydrology. II: Hydrologic applications." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2000)5:2(124), 124-137.
10. Haktanir, T., and Sezen, N. (1990). "Suitability of two-parameter gamma and three-parameter beta distributions as synthetic unit hydrographs in Anatolia." Hydrol. Sci. J., 35(2), 167-184.
11. Haltiner, J. P., and Salas, J. D. (1988). "Short-term forecasting of snowmelt discharge using ARMAX models." J. Amer. Water Resour. Assoc., 24(5), 1083-1089.
12. He, J. X., and Valeo, C. (2009). "Comparative study of ANNs versus parametric methods in rainfall frequency analysis." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2009)14:2(172), 172-184.
13. Hornik, K., Stinchcombe, M., and White, H. (1989). "Multilayer feedforward networks are universal approximators." Neural Networks, 2(5), 359-366.
14. Hu, T. S., Lam, K. C., and Ng, S. T. (2001). "River flow time series prediction with a range dependent neural network." Hydrol. Sci. J., 46(5), 729-745.
15. Huang, G. H., et al. (2007). "An optimization-simulation approach for watershed management under changing climate in the Georgia Basin." Prepared for CCIAD, Earth Sciences Sector, Natural Resources Canada.
16. Huang, G. H., Sun, W., Nie, X. H., Qin, X. S., and Zhang, X. D. (2010). "Development of a decision support system for rural eco-environmental management in Yongxin County, Jiangxi Province, China." Environ. Model. Software, 25(1), 25-42.
17. Iorgulescu, I., and Beven, K. J. (2004). "Nonparametric direct mapping of rainfall-runoffrelationships: An alternative approach to data analysis and modeling?" Water Resour. Res., 40(8), W08403.
18. Islam, M. N., and Sivakumar, B. (2002). "Characterization and prediction of runoffdynamics: A nonlinear dynamical view." Adv. Water Resour., 25(2), 179-190.
19. Jain, A., and Indurthy, S. (2003). "Comparative analysis of event-based rainfall-runoffmodeling techniques-Deterministic, statistical, and artificial neural networks." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699 (2003)8:2(93), 93-98.
20. Jayawardena, A. W., and Gurung, A. B. (2000). "Noise reduction and prediction of hydrometeorological time series: Dynamical systems approach vs. stochastic approach." J. Hydrol., 228(3-4), 242-264.
21. Jayawardena, A. W., and Lai, F. (1994). "Analysis and prediction of chaos in rainfall and stream flow time series." J. Hydrol., 153(1-4), 23-52.
22. Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). "Neural networks for river flow prediction." J. Comput. Civ. Eng., 10.1061/(ASCE)0887-3801(1994)8:2(201), 201-220.
23. Kişi, Ö. (2004). "River flow modeling using artificial neural networks." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699(2004)9:1(60), 60-63.
24. Kişi, Ö. (2007). "Streamflow forecasting using different artificial neural network algorithms." J. Hydrol. Eng., 10.1061/(ASCE)1084-0699 (2007)12:5(532), 532-539.
25. Kůrková, V. (1992). "Kolmogorov's theorem and multilayer neural networks." Neural Networks, 5(3), 501-506.
26. Londhe, S., and Charhate, S. (2010). "Comparison of data-driven modelling techniques for river flow forecasting." Hydrol. Sci. J., 55(7), 1163-1174.
27. Maier, H. R., and Dandy, G. C. (2000). "Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications." Environ. Model. Software, 15(1), 101-124.
28. Moré, J. J. (1978). "The Levenberg-Marquardt algorithm: Implementation and theory." Numer. Anal., 630, 105-116.
29. Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., and Kowalski, S. M. (2004). "Response surface methodology: A retrospective and literature survey." J. Qual. Tech., 36(1), 53-77.
30. Nash, J. E., and Sutcliffe, J. V. (1970). "River flow forecasting through conceptual models part I-A discussion of principles." J. Hydrol., 10(3), 282-290.
31. O'Connell, P. E., Nash, J. E., and Farrell, J. P. (1970). "River flow forecasting through conceptual models part II-The Brosna catchment at Ferbane." J. Hydrol., 10(4): 317-329.
32. Porporato, A., and Ridolfi, L. (1997). "Nonlinear analysis of river flow time sequences." Water Resour. Res., 33(6), 1353-1367.
33. Porporato, A., and Ridolfi, L. (2001). "Multivariate nonlinear prediction of river flows." J. Hydrol., 248(1-4), 109-122.
34. Raman, H., and Sunikumar, N. (1995). "Multivariate modelling of water resources time series using artificial neural networks." Hydrol. Sci. J., 40(2), 145-163.
35. Ranade, A. K., Pandey, M., and Datta, D. (2010). "Stochastic response surface based simulation of ground water modeling." Proc., Int. Conf. on Modeling, Optimization, and Computing, West Bengal, India, AIP Publishing, Melville, New York, 28-30 Oct. 2010.
36. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). "Learning internal representation by error propagation." Parallel distributed processing, Vol. 1: Foundations, MIT Press, Cambridge, MA, 318-362.
37. Salas, J. D., Tabios, G. Q., III, and Bartolini, P. (1985). "Approaches to multivariate modeling of water resources time series." J. Amer. Water Resour. Assoc., 21(4), 683-708.
38. Seber, G. A. F., andWild, C. J. (1989). Nonlinear regression, JohnWiley & Sons, New York.
39. Singh, P., and Deo, M. C. (2007). "Suitability of different neural networks in daily flow forecasting." Appl. Soft Comput., 7(3), 968-978.
40. Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L. (2009). "A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series." J. Hydrol., 374(3-4), 294-306.
41. Zealand, C. M., Burn, D. H., and Simonovic, S. P. (1999). "Short term streamflow forecasting using artificial neural networks." J. Hydrol., 214(1-4), 32-48.