Geomorphology-based genetic programming approach for rainfall-runoff modeling

Journal of Hydroinformatics

Volumn 15, Issue 2, 2013, Pages 427-445

  Nourani, Vahid ^a   Komasi, Mehdi ^a,b   Alami, Mohamad Taghi ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

^b AYATOLLAH BOROUJERDI UNIVERSITY   (Iran)

Author keywords

Eel River watershed; Genetic programming; Geomorphology; Rainfall runoff modeling; Spatiotemporal modeling

Indexed keywords

EID: 84876399717     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2012.113     Document Type: Article

References (51)

1. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology 2000 Artificial neural networks in hydrology. II: Hydrologic applications. J. Hydrol. Eng. 5 (2), 124-137.
2. Aytek, A. & Kisi, O. 2008 A genetic programming approach to suspended sediment modeling. J. Hydrol. 351, 288-298.
3. Azamathulla, H. Md. 2012 Gene-expression programming to predict scour at a bridge abutment. J. Hydroinf. 14 (2), 324-333.
4. Babovic, V. & Keijzer, M. 2002 Rainfall-runoff modelling based on genetic programming. Nordic Hydrol. 33 (5), 331-334.
5. Babovic, V., Keijzer, M., Aguilera, D. R. & Harrington, J. 2001 An evolutionary approach to knowledge induction: Genetic programming in hydraulic engineering. World Water and Environmental Resources Congress, Orlando, FL, USA
6. Beven, K. J. & Kirkby, M. J. 1979 A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. J. 24 (1,3), 43-69.
7. Cousin, N. & Savic, D. A. 1997 A rainfall-runoff model using genetic programming. Centre for Systems and Control Engineering. Report No. 97/03, School of Engineering, University of Exeter, Exeter, UK, 70 pp.
8. Demirel, M. C, Booij, M. J. & Kahya, E. 2011 Validation of an ANN flow prediction model using a multi-station cluster. J. Hydrol. Eng. 16 (2), 262-271.
9. Dorado, J., Rabunal, J. R., Pazos, A., Rivero, D., Santos, A. & Puertas, J. 2003 Prediction and modelling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Appl. Artif. Intell. 17 (4), 329-343.
10. Drecourt, J. P. 1999 Application of neural networks and genetic programming to rainfall-runoff modeling. D2K Technical Report 0699-1-1, Danish Hydraulic Institute, Denmark.
11. Falco, D., Cioppa, A. D. & Tarantino, E. 2005 A genetic programming system for time series prediction and its application to El Niño forecast. Appl. Soft Comput. 32, 151-162.
12. Garbrecht, J. D. 2006 Comparison of three alternative ANN designs for monthly rainfall-runoff simulation. J. Hydrol. Eng. 11 (5), 502-505.
13. Giustolisi, O. 2004 Using genetic programming to determine Chezy resistance coefficient in corrugated channels. J. Hydroinf. 6, 157-173.
14. Guven, A. & Kisi, O. 2011 Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming. Water Resour. Manage. 25 (2), 691-704.
15. Jain, A., Sudheer, K P. & Srinivasulu, S. 2004 Identification of physical processes inherent in artificial neural network rainfall-runoff models. Hydrol. Process. 18, 571-581.
16. Jayawardena, A. W., Muttil, N. & Lee, J. H. W. 2006 Comparative analysis of data-driven and GIS-based conceptual rainfall-runoff model. J. Hydrol. Eng. 11 (1), 1-89.
17. Kisi, O. & Guven, A. 2010 A machine code-based genetic programming for suspended sediment concentration estimation. Adv. Eng. Softw. 41 (7-8), 939-945.
18. Koza, J. R 1992 Genetic Programming on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA
19. Legates, D. R. & McCabe Jr, G. J. 1999 Evaluating the use of goodness-of-fit measures in hydrologic and hydro-climatic model validation. Water Resour. Res. 35 (1), 233-241.
20. Luk, K. C, Ball, J. E. & Sharma, A. 2001 An application of artificial neural networks for rainfall forecasting. Math. Comput. Modell. 33, 683-693.
21. Makkeasorn, A., Chang, N. B. & Zhou, X. 2008 Short-term stream flow forecasting with global climate change implications - a comparative study between genetic programming and neural network models. J. Hydrol. 352, 336-354.
22. Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. 2010 Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55 (7), 1175-1189.
23. Mutlu, E., Chaubey, I., Hexmoor, H. & Bajwa, S. G. 2008 Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol. Process. 22, 5097-5106.
24. Muttil, N. & Liong, S. Y. 2004 Physically interpretable rainfall-runoff models using genetic programming. 6th International Conference on Hydroinformatics, Singapore, pp. 1655-1662.
25. Nash, J. E. & Sutcliffe, J. V. 1970 River flow forecasting through conceptual models part I. A discussion of principles. J. Hydrol. 10 (3), 282-290.
26. Nourani, V. 2009 Reply to comment on An ANN-based model for spatiotemporal groundwater level forecasting.' Hydrol. Process. 24 (3), 370-371.
27. Nourani, V. & Kalantari, O. 2010 Integrated artificial neural network for spatiotemporal modeling of rainfall-runoff-sediment processes. Environ. Eng. Sci. 27 (5), 411-422.
28. Nourani, V., Monadjemi, P. & Singh, V. P. 2007 Liquid analog model for laboratory simulation of rainfall-runoff process. J. Hydrol. Eng. 12 (3), 246-255.
29. Nourani, V., Mogaddam, A. A. & Nadiri, A. O. 2008 An ANN based model for spatiotemporal groundwater level forecasting. Hydrol. Process. 22, 5054-5066.
30. Nourani, V., Komasi, M. & Mano, A. 2009 A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour. Manage. 23, 2877-2894.
31. Nourani, V., Ejlali, R. G & Alami, M. T. 2011a Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural network-geostatistics model: a case study. Environ. Eng. Sci. 28 (3), 217-228.
32. Nourani, V., Kisi, Ö. & Komasi, M. 2011b Two hybrid artificial intelligence approaches for modeling rainfall-runoff process. J. Hydrol. 402 (1-2), 41-59.
33. Nourani, V., Komasi, M. & Alami, M. T. 2012a A hybrid wavelet-genetic programming approach to optimize ANN modelling of rainfall-runoff process. J. Hydrol. Eng. 17(6), 724-741.
34. Nourani, V., Kalantari, O. & Hosseini Baghanam, A. 2012b Two semi-distributed ANN-based models for estimation of suspended sediment load. J. Hydrol. Eng. Published online.
35. Rajurkar, M. P., Kothyari, U. C. & Chaube, U. C 2004 Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol. 285, 96-113.
36. Rodríguez-Vázquez, K, Arganis-Juárez, M. L., Cruickshank- Villanueva, C. & Domínguez-Mora, R 2012 Rainfall-runoff modelling using genetic programming. J. Hydroinf. 4 (1), 108-121.
37. Sarangi, A., Madramootoo, C A, Enright, P., Prasher, S. O. & Patel, R. M. 2005 Performance evaluation of ANN and geomorphology- based models for runoff and sediment yield prediction for a Canadian watershed. Curr. Sci. 89 (12), 2022-2033.
38. Savic, D. A., Walters, G A. & Davidson, J. 1999 A genetic programming approach to rainfall-runoff modeling. Water Resour. Manage. 13, 219-231.
39. Selle, B. & Muttil, N. 2011 Testing the structure of a hydrological model using genetic programming. J. Hydrol. 397 (1-2), 1-9.
40. Sharifi, S., Sterling, M. & Knight, D. W. 2011 Prediction of end-depth ratio in open channels using genetic programming. J. Hydroinf. 13 (1), 36-48.
41. Sivanandam, S. N. & Deepa, S. N. 2008 Introduction to Genetic Algorithms. Springer, Berlin Heidelberg, New York.
42. Sivapragasam, C, Vincent, P. & Vasudevan, G 2007 Genetic programming model for forecast of short and noisy data. Hydrol. Process. 21, 266-272.
43. Sreekanth, J. & Datta, B. 2011 Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management. Water Resour. Manage. 25 (13), 3201-3218.
44. Sudheer, K P., Gosain, A. K & Ramasastri, K S. 2000 A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Process. 16 (6), 1325-1330.
45. Tokar, A. S. & Johnson, P. A. 1999 Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 4, 232-239.
46. Tokar, A. S. & Markus, M. 2000 Precipitation-runoff modeling using artificial neural networks and conceptual models. J. Hydrol. Eng. 5 (2), 156-161.
47. Whigham, P. A. & Crapper, P. F. 2001 Modeling rainfall-runoff using genetic programming. Math. Comput. Modell. 33, 707-721.
48. Wilby, R L., Abrahart, R J. & Dawson, C. W. 2003 Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrol. Sci. J. 48 (2), 163-181.
49. Wang, W., Xu, D., Qiu, L. & Ma, J. 2009 Genetic programming for modelling long-term hydrological time series. Fifth International Conference, Natural Computation, Tianjin, China, 4, pp. 265-269.
50. Xu, Q., Chen, Q. & Li, W. 2011 Application of genetic programming to modeling pipe failures in water distribution systems. J. Hydroinf. 13 (3), 419-428.
51. Zhang, B. & Govindaraju, R S. 2003 Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds. J. Hydrol. 273, 18-34.