Skip to main content
SpringerLink
Log in

Daily pan evaporation modeling using linear genetic programming technique

  • Original Paper
  • Published:
Irrigation Science Aims and scope Submit manuscript

Abstract

This paper investigates the ability of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily pan evaporation modeling. The daily climatic data, air temperature, solar radiation, wind speed, pressure and humidity of three automated weather stations, Fresno, Los Angeles and San Diego in California, are used as inputs to the LGP to estimate pan evaporation. The LGP estimates are compared with those of the Gene-expression programming (GEP), which is another branch of GP, multilayer perceptrons (MLP), radial basis neural networks (RBNN), generalized regression neural networks (GRNN) and Stephens–Stewart (SS) models. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R 2) statistics. Based on the comparisons, it was found that the LGP technique could be employed successfully in modeling evaporation process from the available climatic data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F (eds) Proc., 2nd Int. Symp. on information Theory. Academiai Kiado, Budapest, Hungary, pp 267–281

  • Al-Ghobari HM (2000) Estimation of reference evapotranspiration for southern region of Saudi Arabia. Irrig Sci 19(2):81–86

    Article  Google Scholar 

  • Aytek A, Kisi O (2008) A genetic programming approach to suspended sediment modeling, J. Hydrology 351(3–4):288–298

    Article  Google Scholar 

  • Babovic V, Keijzer M (2002) Declarative and preferential bias in GEP-based scientific discovery. Genet Program Evolvable Mach 3(1):41–79

    Google Scholar 

  • Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming: an introduction. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Brameier M (2004) On linear genetic programming. Ph.D. thesis. University of Dortmund

  • Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evol Comput 5:17–26

    Article  Google Scholar 

  • Brutsaert WH (1982) Evaporation into the atmosphere. D. Reidel Publishing Company, Dordrecht

    Google Scholar 

  • Dorado J, Rabunal JR, Pazos A, Rivero D, Santos A, Puertas J (2003) Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Appl Artif Intell 17:329–343

    Article  Google Scholar 

  • Ferreira C (2001a) Gene expression programming in problem solving. In: Proceedings of the 6th online world conference on soft computing in industrial applications (invited tutorial)

  • Ferreira C (2001b) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    Google Scholar 

  • Giustolisi O (2004) Using genetic programming to determine Chezy resistance coefficient in corrugated channels. J Hydroinformatics 6(3):157–173

    Google Scholar 

  • Gundekar HG, Khodke UM, Sarkar S, Rai RK (2008) Evaluation of pan coefficient for reference crop evapotranspiration for semi-arid region. Irrig Sci 26(2):169–175

    Article  Google Scholar 

  • Guven A (2009) Linear genetic programming for time-series modeling pf daily flow rate. J Earth Syst Sci 118(2):137–146

    Article  Google Scholar 

  • Guven A, Aytek A (2009) New approach for stage-discharge relationship: gene-expression programming. J Hydrol Eng 14(8):812–820

    Google Scholar 

  • Guven A, Gunal M (2008a) Prediction of scour downstream of grade-control structures using neural networks. J Hydraulic Eng 134(11):1656–1660

    Article  Google Scholar 

  • Guven A, Gunal M (2008b) Genetic programming for prediction of local scour downstream of grade-control structures. J Irrig Drainage Eng 134(2):241–249

    Article  Google Scholar 

  • Guven A, Kişi Ö (2009) Suspended sediment modeling using linear genetic modeling. Hydrol Sci J (under review)

  • Guven A, Gunal M, Cevik A (2006) Prediction of pressure fluctuations on stilling basins. Can J Civil Eng 33(11):1379–1388

    Article  Google Scholar 

  • Guven A, Aytek A, Yuce MI, Aksoy H (2007) Genetic programming-based empirical model for daily reference evapotranspiration estimation. Clean-Soil Air Water 36(10–11):905–912

    Google Scholar 

  • Guven A, Azamathulla HMd, Zakaria NA (2009) Linear genetic programming for prediction of circular pile scour. J Ocean Eng 36(12–13):985–991

    Article  Google Scholar 

  • Keskin ME, Terzi O (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65–70

    Article  Google Scholar 

  • Kisi O (2009a) Daily pan evaporation modeling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 23:213–223

    Article  Google Scholar 

  • Kisi O (2009b) Modeling monthly evaporation using two different neural computing techniques. Irrig Sci 27(5):417–430

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge

    Google Scholar 

  • Kumar M, Bandyopadhyay A, Raghuwanshi NS, Singh R (2008) Comparative study of conventional and artificial neural network-based ETo estimation models. Irrig Sci 26(6):531–545

    Article  Google Scholar 

  • Nordin P (1994) A compiling genetic programming system that directly manipulates the machine-code. In: Kinnear KE (ed) Advances in genetic programming. MIT Press, Cambridge, pp 311–331

    Google Scholar 

  • Nordin P (1997) Evolutionary program induction of binary machine code and its applications. Ph.D. dissertation: Dept. Comput. Sci., Univ. Dortmund

  • Nordin P, Banzhaf W (1995) Evolving turing-complete programs for a register machine with self-modifying code. In: Eshelman L (ed) Proceedings of the 6th international conference of genetic algorithms. Morgan Kaufmann, Pittsburgh, PA, USA, pp 318–325

  • Oltean M, Grosan C (2003) A comparison of several linear genetic programming techniques. Complex Syst 14(1):1–29

    Google Scholar 

  • Rabunal JR, Puertas J, Suarez J, Rivero D (2007) Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol Process 21:476–485

    Article  Google Scholar 

  • Rahimikhoob A (2009) An evaluation of common pan coefficient equations to estimate reference evapotranspiration in a subtropical climate (north of Iran). Irrig Sci 27(4):289–296

    Article  Google Scholar 

  • Rissanen J (1978). Modeling by the shortest data description. Automatica 14:465–471

    Google Scholar 

  • Stephens JC, Stewart EH (1963) A comparison of procedures for computing evaporation and evapotranspiration. Publication 62, international association of scientific hydrology. International Union of Geodynamics and Geophysics, Berkeley, CA, pp 123–133

  • Tabari H, Marofi S, Sabziparvar A-A (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28(5):399–406. doi:10.1007/s00271-009-0201-0

    Google Scholar 

  • Whigham PA, Crapper PF (2001) Modeling rainfall-runoff using genetic programming. Math Comput Model 33:707–721

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, Hydraulics Division, Gaziantep University, 27310, Gaziantep, Turkey

    Aytac Guven

  2. Civil Engineering Department, Hydraulics Division, Erciyes University, 38039, Kayseri, Turkey

    Özgür Kişi

Authors
  1. Aytac Guven
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Özgür Kişi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aytac Guven.

Additional information

Communicated by S. Azam-Ali.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guven, A., Kişi, Ö. Daily pan evaporation modeling using linear genetic programming technique. Irrig Sci 29, 135–145 (2011). https://doi.org/10.1007/s00271-010-0225-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00271-010-0225-5

Keywords

Search

Navigation