Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique

Journal of Hydrology

Volumn 505, Issue , 2013, Pages 240-249

  Danandeh Mehr, Ali ^a   Kahya, Ercan ^a   Olyaie, Ehsan ^b  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Data pre processing; Feed forward neural networks; Hydrologic models; Linear genetic programming; Streamflow prediction; Wavelet transform

Indexed keywords

ACCURATE PREDICTION; ARITHMETIC FUNCTIONS; DATA PREPROCESSING; HYDROLOGIC MODELS; HYDROLOGICAL PROCESS; LINEAR GENETIC PROGRAMMING; ROOT MEAN SQUARE ERRORS; STREAMFLOW PREDICTION;

CATCHMENTS; FORECASTING; GENETIC PROGRAMMING; MATHEMATICAL MODELS; MEAN SQUARE ERROR; MODELS; NEURAL NETWORKS; NUMBER THEORY; QUALITY MANAGEMENT; RUNOFF; TIME SERIES; WAVELET TRANSFORMS;

STREAM FLOW;

ACCURACY ASSESSMENT; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CATCHMENT; DATA PROCESSING; HYDROLOGICAL MODELING; LINEAR PROGRAMING; RESEARCH WORK; STREAMFLOW; TIME SERIES; WATER MANAGEMENT; WATER QUALITY; WAVELET ANALYSIS;

EID: 84886301699     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.10.003     Document Type: Article

References (42)

1. Abrahart R.J., Anctil F., Coulibaly P., Dawson C.W., Mount N.J., See L.M., Shamseldin A.Y., Solomatine D.P., Toth E., Wilby R.L. Two decades of anarchy? Emerging themes and outstanding challenges for neural network modelling of surface hydrology. Prog. Phys. Geogr. 2012, 36(4):480-513.
2. 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, 390:85-91.
3. Alavi, A.H., Gandomi, A.H., Gandomi, M., Comment on Sivapragasam, C., Maheswaran, R., Venkatesh, V,. 2008. Genetic programming approach for flood routing in natural channels. Hydrol. Process. 24, 2010, pp. 798-799.
4. Anctil F., Tape D. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J. Environ. Eng. Sci. 2004, 3:121-128.
5. ASCE Task Committee Artificial neural networks in hydrology: hydrologic applications. J. Hydrol. Eng. 2000, 5(2):124-137.
6. Banzhaf, W., Nordin, P., Keller, R., Francone, F.D., 1998. Genetic programming-an introduction on the automatic evolution of computer programs and its application. dpunkt/Morgan Kaufmann: Heidelberg, San Francisco, 1998.
7. Besaw L.E., Rizzo D.M., Bierman P.R., Hackett W.R. Advances in ungauged streamflow prediction using artificial neural networks. J. Hydrol. 2010, 386:27-37.
8. Brameier M., Banzhaf W. Linear Genetic Programming 2007, Springer Science+Business Media LLC., New York.
9. Can I., Tosunogulu F., Kahya E. Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin. Turkey. Water Environ. J. 2012, 26:567-576.
10. Dolling O.R., Varas E.A. Artificial neural networks for streamflow prediction. J. Hydraul. Res. 2002, 40(5):547-554.
11. Dorado J., Rabunal J.R., Pazos A., Rivero D., Santos A., Puertas J. Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Appl. Artif. Intell. 2003, 17:329-343.
12. Francone, F.D., 1998. DiscipulusTM software owner's manual, version 3.0 DRAFT. Machine Learning Technologies Inc., Littleton CO., USA, 1998.
13. Gandomi A.H., Alavi A.H. A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput. Appl. 2012, 21:171-187.
14. Ghorbani M., Khatibi R., Aytek A., Makarynskyy O. Sea water level forecasting using genetic programming and artificial neural networks. Comput. Geosci. UK 2010, 36(5):620-627.
15. Guven A. Linear genetic programming for time-series modelling of daily flow rate. J. Earth Syst. Sci. 2009, 118(2):137-146.
16. Kim C.H., Aggarwal R. Wavelet transform in power systems Part 1. Power Eng. J. 2000, 14(2):81-87.
17. Kisi O., Cigizoglu H.K. Comparison of different ANN techniques in river flow prediction. Civ. Eng. Environ. Sys. 2007, 24(3):211-231.
18. Kisi O. Stream flow forecasting using neuro-wavelet technique. Hydrol. Process. 2008, 22:4142-4152.
19. Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection 1992, MIT Press, Cambridge (MA).
20. Kuchment L.S., Demidov V.N., Naden P.S., Cooper D.M., Broadhurst P. Rainfall-runoff modelling of the Ouse basin, North Yorkshire: an application of a physically based distributed model. J. Hydrol. 1996, 181(1-4):323-342.
21. Labat D. Recent advances in wavelet analyses: Part 1 - a review of concepts. J. Hydrol. (Amsterdam) 2005, 314(1-4):275-288.
22. Londhe S., Charhate S. Comparison of data-driven modelling techniques for river flow forecasting. Hydrolog. Sci. J. 2010, 55(7):1163-1174.
23. Mallat S. A Wavelet Tour of Signal Processing 1998, Academic Press, San Diego (CA). second ed.
24. 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.
25. Nourani V., Komasi M., Mano A. A multivariate ANN wavelet approach for rainfall-runoff modelling. Water Resour. Manage. 2009, 23:2877-2894.
26. Nourani V., Kisi O., Komasi M. Two hybrid artificial intelligence approaches for modeling rainfall-runoff process. J. Hydrol. 2011, 402:41-59.
27. Nourani V., Komasi M., Alami M.T. Hybrid wavelet-genetic programming approach to optimize ANN modelling of rainfall-runoff process. J. Hydrol. Eng. 2012, 17(6):724-741.
28. Nourani V., Hosseini Baghanam A., Adamowski J., Gebremichael M. Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. J. Hydrol. 2013, 476:228-243.
29. Onderka M., Banzhaf S., Scheytt T., Krein A. Seepage velocities derived from thermal records using wavelet analysis. J. Hydrol. 2013, 479:64-74.
30. Ozger M. Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Eng. 2010, 37:1443-1451.
31. Partal T., Kisi O. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J. Hydrol. 2007, 342:199-212.
32. Poli, R., Langdon, W.B., McPhee, N.F., 2008. A field guide to genetic programming. < (published) and <(freely available). (with contributions by Koza, J.R.). http://www.gp-field-guide.org.uk.
33. Priddy K.L., Keller P.B. Artificial Neural Networks an Introduction 2005, SPIE Press, Washington.
34. Rabunal J.R., Puertas J., Suarez J., Rivero D. Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol. Process. 2007, 21:476-485.
35. Rajaee T., Mirbagheri S.A., Nourani V., Alikhani A. Prediction of daily suspended sediment load using wavelet and neuro-fuzzy combined model. Int. J. Environ. Sci. Technol. 2010, 7(1):93-110.
36. Rathinasamy M., Khosa R. Multi-scale nonlinear model for monthly streamflow forecasting: a wavelet-based approach. J. Hydroinform. 2012, 14(2):424-442.
37. Sajikumar N., Thandaveswara B.S. A non-linear rainfall-runoff model using an artificial network. J. Hydrol. 1999, 216(4):32-55.
38. Sivapragasam C., Maheswaran R., Veena V. Genetic programming approach for flood routing in natural channels. Hydrol. Process. 2008, 22:623-628.
39. Sreekanth J., Datta B. Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization. Water Resour. Res. 2011, 47:W04516.
40. Tahershamsi A., Majdzade Tabatabai M.R., Shirkhani R. An evaluation model of artificial neural network to predict stable width in gravel bed rivers. Int. J. Environ. Sci. Technol. 2012, 9:333-342.
41. Wang W.C., Chau K.W., Cheng C.T., Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol. 2009, 374:294-306.
42. Whigham P.A., Crapper P.F. Modelling rainfall runoff using genetic programming. Math. Comput. Model. 2001, 33:707-721.