Comparative study of different wavelet based neural network models for rainfall-runoff modeling

Journal of Hydrology

Volumn 515, Issue , 2014, Pages 47-58

  Shoaib, Muhammad ^a   Shamseldin, Asaad Y ^a   Melville, Bruce W ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

Artificial neural network; Rainfall runoff modeling; Transformation; Wavelet

Indexed keywords

DISCRETE WAVELET TRANSFORMS; METADATA; MULTILAYERS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RAIN; RUNOFF; SIGNAL RECONSTRUCTION; WAVELET DECOMPOSITION;

ARTIFICIAL NEURAL NETWORK MODELS; DISCRETE WAVELET TRANSFORMATION; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RAINFALL-RUNOFF MODELING; TRANSFORMATION; WAVELET; WAVELET-BASED NEURAL NETWORK; COMPARATIVE STUDIES; DIFFERENT WAVELETS;

MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; PERFORMANCE ASSESSMENT; RAINFALL-RUNOFF MODELING; TIME SERIES; WAVELET ANALYSIS; COMPARATIVE STUDY;

EID: 84900000488     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.04.055     Document Type: Article

References (43)

1. Adamowski J., Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J. Hydrol. 2010, 390(1-2):85-91.
2. Addison P.S. The Illustrated Wavelet Transform Handbook 2002, Institute of Physics Publishing, London.
3. Aussem A., Campbell J., Murtagh F. Wavelet-based feature extraction and decomposition strategies for financial forecasting. J. Comput. Intell. Finance 1998, 6(2):5-12.
4. Cannas B., Fanni A., See L., Giuliana Data pre-processing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys. Chem. Earth Parts A/B/C 2006, 31(18):1164-1171.
5. Daubechies, I., 1988. orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. XLI, 909-996.
6. Daubechies, I., 1992. Ten lectures on wavelets (CBMS-NSF regional conference series in applied mathematics). Soc. Indust. Appl. Math.
7. Goswami M., O'Connor K.M., Bhattarai K.P., Shamseldin A.Y. Assessing the performance of eight real-time updating models and procedures for the Brosna River. Hydrol. Earth Syst. Sci. 2005, 9(4).
8. Haar A. Zur Theories der orthogonalen Funktionensysteme. Math. Ann. 1910, 69(3):331-371.
9. Hammerstrom D. Working with neural networks. Spectrum IEEE 1993, 30(7):46-53.
10. Jacquin A.P., Shamseldin A.Y. Development of rainfall-runoff models using Takagi-Sugeno fuzzy inference systems. J. Hydrol. 2006, 329(1):154-173.
11. Jacquin A.P., Shamseldin A.Y. Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models. Hydrol. Earth Syst. Sci. 2009, 13(1).
12. Jain A., Kumar A.M. Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comput. 2007, 7(2):585-592.
13. Karunanithi N., Grenney W.J., Whitley D., Bovee K. Neural networks for river flow prediction. J. Comput. Civil Eng. 1994, 8(2):201-220.
14. Kasabov, N.K., 1996. Foundations of neural networks, fuzzy systems, and knowledge engineering. United States of America, Massachusetts Institute of Technology.
15. Kisi O. Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resour. Manage 2011, 25(2):579-600.
16. Kisi O., Shiri J., Tombul M. Modeling rainfall-runoff process using soft computing techniques. Comput. Geosci. 2013, 51:108-117.
17. Labat D., Ababou R., Mangin A. Rainfall-runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses. J. Hydrol. 2000, 238(3):149-178.
18. Labat D., Ababou R., Mangin A. Introduction of wavelet analyses to rainfall/runoffs relationship for a Karstic Basin: the case of Licq-Atherey Karstic System (France). Ground Water 2001, 39(4):605-615.
19. Legates D.R., McCabe G.J. Evaluating the use of "goodness of fit" measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35(1):233-241.
20. Luo, F.-L., Unbehauen, R., Applied Neural Networks for Signal Processing, Cambridge University Press.
21. Maheswaran R., Khosa R. Comparative study of different wavelets for hydrologic forecasting. Comput. Geosci. 2012, 46:284-295.
22. McGarry, K.J., Wermter, S., MacIntyre, J., 1999. Knowledge extraction from radial basis function networks and multilayer perceptron. In: International Joint Conference on Neural Networks, 1999. IJCNN'99. IEEE.
23. Meyer, Y., 1985. Principe d'incerlitude, bases hilbertiennes et algebres d'operateurs., Asterisque, Societe Mathematique de France, Paris, France.
24. Meyer Y. Ondelettes et Opérateurs I-Ondelettes 1990, Hermann, Paris.
25. Misiti, Y., Oppenheim, G., Poggi, J.M., 2012(b). Wavelet Toolbox User's Guide, The Math Works, Inc., Apple Hill Drive, Natick, MA 01760-2098.
26. Nakken M. Wavelet analysis of rainfall-runoff variability isolating climatic from anthropogenic patterns. Environ. Modell. Softw. 1999, 14(4):283-295.
27. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models. Part 1: A discussion of principles. J. Hydrol. 1970, 10(3):282-290.
28. Nourani V., Alami M.T., Aminfar M.H. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 2009, 22(3):466-472.
29. Nourani V., Komasi M., Mano A. A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour. Manage 2009, 23(14):2877-2894.
30. Nourani V., Kisi Ö., Komasi M. Two hybrid Artificial Intelligence approaches for modelling rainfall-runoff process. J. Hydrol. 2011, 402(1-2):41-59.
31. Phukoetphim P., Shamseldin A.Y., Bruce W.M. Knowledge extraction from artificial neural network for rainfall-runoff models combination system. J. Hydrol. Eng 2013.
32. Rajukar M.P., Kothyari U.C., Chaube U.C. Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol. 2004, 285(1-4):96-113.
33. Shamseldin A.Y. Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 1997, 199(3-4):272-294.
34. Shamseldin A.Y., O'Connor K.M. A nearest neighbour linear perturbation model for river flow forecasting. J. Hydrol. 1996, 179(1):353-375.
35. Shamseldin A.Y., O'Connor K.M. A real-time combination method for the outputs of different rainfall-runoff models. Hydrol. Sci. J. 1999, 44(6):895-912.
36. Shamseldin A.Y., O'Connor K.M., Liang G.C. Methods for combining the outputs of different rainfall-runoff models. J. Hydrol. 1997, 197(1-4):203-229.
37. Shamseldin A.Y., Nasr A.E., O'Connor K.M. Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecasting. Hydrol. Earth Syst. Sci. Discuss. 2002, 6(4):671-684.
38. Shamseldin A.Y., O'Connor K.M., Nasr A.E. A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models. Hydrol. Sci. J. 2007, 52(5):896-916.
39. Singh R.M. Wavelet-ANN model for Flood Events. Adv. Intell. Soft Comput. 2012, 131:165-175.
40. Srinivasulu S., Jain A. A comparative analysis of training methods for artificial neural network rainfall-runoff models. Appl. Soft Comput. 2006, 6(3):295-306.
41. Tiwari M.K., Chatterjee C. Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. J. Hydrol. 2010, 394(3-4):458-470.
42. Tokar A.S., Johnson P.A. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 1999, 4(3):232-239.
43. Wang W., Ding J. Wavelet network model and its application to the prediction of hydrology. Nat. Sci. 2003, 1(1):67-71.