Skip to main content
SpringerLink
Log in

Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

The applicability of fuzzy genetic (FG) approach in modeling reference evapotranspiration (ET0) is investigated in this study. Daily solar radiation, air temperature, relative humidity and wind speed data of two stations, Isparta and Antalya, in Mediterranean region of Turkey, are used as inputs to the FG models to estimate ET0 obtained using the FAO-56 Penman–Monteith equation. The FG estimates are compared with those of the artificial neural networks (ANN). Root mean-squared error, mean absolute error and determination coefficient statistics were used as comparison criteria for the evaluation of the models’ accuracies. It was found that the FG models generally performed better than the ANN models in modeling ET0 of Mediterranean region of Turkey.

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
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abghari H, Ahmadi H, Besharat S, Rezaverdinejad V (2012) Prediction of daily pan evaporation using wavelet neural networks. Water Resour Manage 26:3639–3652

    Article  Google Scholar 

  • Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manage 19:145–161

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration guidelines for computing crop water requirements, FAO Irrigation and Drainage, Paper No. 56, Food and Agriculture Organization of the United Nations, Rome

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

    Book  Google Scholar 

  • Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398:292–302

    Article  Google Scholar 

  • Demirtas C, Buyukcangaz H, Yazgan S, Candogan BN (2007) Evaluation of evapotranspiration estimation methods for sweet cherry trees (Prunus avium) in Sub-humid Climate. Pak J Biol Sci 10(3):462–469

    Article  Google Scholar 

  • Dinpashoh Y, Jhajharia D, Fakheri-Fard A, Singh VP, Kahya E (2011) Trends in reference evapotranspiration over Iran. J Hydrol 399:422–433

    Article  Google Scholar 

  • Gavilan P, Estevaz J, Berengena J (2008) Comparison of standardized reference evapotranspiration equations in Southern Spain. J Irrig Drain Eng 134(1):1–12

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search: optimization and machine learning. Addison-Wesley, Reading

    Google Scholar 

  • Haykin S (1998) Neural networks - a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River, pp 26–32

    Google Scholar 

  • Hecht-Nielsen R (1991) Neurocomputing. Addison-Wesley Publ Co., New York

    Google Scholar 

  • Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol Process 22:2225–2234

    Article  Google Scholar 

  • Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practices No. 70., ASCE, New York, NY, 360 pp

  • Jhajharia D, Ali MI, Deb Barma S, Durbude DG, Kumar R (2009) Assessing reference evapotranspiration by temperature-based methods for humid regions of Assam. J Indian Water Resour Soc 29(2):1–8

    Google Scholar 

  • Jhajharia D, Dinpashoh Y, Kahya E, Singh VP, Fakheri-Fard A (2012) Trends in reference evapotranspiration in the humid region of northeast India. Hydrol Process 26:421–435

    Article  Google Scholar 

  • Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrol Sci J 49(6):1001–1010

    Google Scholar 

  • Khoob AR (2008a) Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrig Sci 26(3):253–259

    Article  Google Scholar 

  • Khoob AR (2008b) Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment. Irrig Sci 27(1):35–39

    Article  Google Scholar 

  • Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modelling. J Hydrol 351:299–317

    Article  Google Scholar 

  • Kim S, Shiri J, Kisi O (2012) Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour Manage 26(11):3231–3249

    Google Scholar 

  • Kim S, Shiri J, Kisi O, Singh VP (2013) Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resour Manage 27(7):2267–2286

    Google Scholar 

  • Kisi O, Uncuoglu E (2005) Comparison of three backpropagation training algorithms for two case studies. Indian J Eng Mater Sci 12:443–450

    Google Scholar 

  • Kisi O (2006a) Evapotranspiration estimation using feed-forward neural networks. Nord Hydrol 37(3):247–260

    Article  Google Scholar 

  • Kisi O (2006b) Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 51(6):1092–1105

    Article  Google Scholar 

  • Kisi O (2007a) Evapotranspiration modelling from climatic data using a neural computing technique. Hydrol Process 21:1925–1934

    Article  Google Scholar 

  • Kisi O (2007b) Streamflow forecasting using different artificial neural network algorithms. ASCE J Hydrol Eng 12(5):532–539

    Article  Google Scholar 

  • Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22:1449–2460

    Google Scholar 

  • Kisi O, Cimen M (2009) Evapotranspiration modelling using support vector machines. Hydrol Sci J 54(5):918–928

    Article  Google Scholar 

  • Kisi O, Ozturk O (2007) Adaptive neuro-fuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng 133(4):368–379

    Article  Google Scholar 

  • Kisi O, Yildirim G (2005a) Discussion of ‘estimating actual evapotranspiration from limited climatic data using neural computing technique’ by K.P. Sudheer; A.K. Gosain; and K.S. Ramasastri. J Irrig Drain Eng 131(2):219–220

    Article  Google Scholar 

  • Kisi O, Yildirim G (2005b) Discussion of ‘forecasting of reference evapotranspiration by artificial neural networks’ by S. Trajkovic; B. Todorovic; and M. Stankovic. J Irrig Drain Eng 131(4):390–391

    Article  Google Scholar 

  • Kiszka JB, Kochanskia ME, Sliwinska DS (1985a) The influence of some fuzzy implication operators on the accuracy of fuzzy model, part I. Fuzzy Set and Syst 15:111–128

    Google Scholar 

  • Kiszka JB, Kochanskia ME, Sliwinska DS (1985b) The influence of some fuzzy implication operators on the accuracy of fuzzy model, part II. Fuzzy Set and Syst 15:223–240

    Google Scholar 

  • Kosko B (1993) Fuzzy thinking: the new science of fuzzy logic. Hyperion, New York

    Google Scholar 

  • Kumar M, Bandyopadhyay A, Rahguwanshi 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 

  • Kumar M, Raghuwanshi NS, Singh R (2009) Development and validation of GANN model for evapotranspiration estimation. J Hydrol Eng 14(2):131–140

    Article  Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R (2011) Artificial neural networks approach in evapotranspiration modeling: a review. Irrig Sci 29:11–25

    Article  Google Scholar 

  • Landeras G, Ortiz-Barredo A, Lopez JJ (2009) Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. J Irrig Drain Eng 135(3):323–334

    Article  Google Scholar 

  • Marti P, Manzano J, Royuela A (2011a) Assessment of a 4-input artificial neural network for ETo estimation through data set scanning procedures. Irrig Sci 29:181–195

    Article  Google Scholar 

  • Marti P, Gonzalez-Altozano P, Gasque M (2011b) Reference evapotranspiration estimation without local climatic data. Irrig Sci 29:479–495

    Article  Google Scholar 

  • McVicar TR, Roderick ML, Donohue RJ, Li LT, Van Niel TG, Thomas A, Grieser J, Jhajharia D, Himri Y, Mahowald NM, Mescherskaya AV, Kruger AC, Rehman S, Dinpashoh Y (2012) Global review and synthesis of trends in observed terrestrial near-surface wind speeds: implications for evaporation. J Hydrol 416–417:182–205

    Article  Google Scholar 

  • Naoum S, Tsanis IK (2003) Hydroinformatics in evapotranspiration estimation. Environ Model Softw 18:261–271

    Article  Google Scholar 

  • Perugu M, Singam AJ, Kamasani CSR (2013) Multiple linear correlation analysis of daily reference evapotranspiration. Water Resour Manag 27:1489–1500

    Article  Google Scholar 

  • Preis A, Ostfeld A (2008) Multiobjective contaminant sensor network design for water distribution systems. J Water Resour Plann Manage 134(4):366–377

    Google Scholar 

  • Russel SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Resour Plann Manage ASCE 122(3):165–170

    Google Scholar 

  • Sanikhani H, Kisi O, Nikpour MN, Dinpashoh Y (2012) Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques. Water Resour Manag 26:4347–4365

    Article  Google Scholar 

  • Scheffé H (1959) The analysis of variance. Wiley, New York

    Google Scholar 

  • Şen Z (1998) Fuzzy algorithm for estimation of solar irridation from sunshine duration, Sol. Energy 63(1):39–49

    Google Scholar 

  • Smith M, Allen R, Pereira L (1997) Revised FAO methodology for crop water requirements. Land and Water Development Division, FAO, Rome

    Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218

    Article  Google Scholar 

  • Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng 131(4):316–323

    Article  Google Scholar 

  • Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting reference evapotranspiration by artificial neural networks. J Irrig Drain Eng 129(6):454–457

    Article  Google Scholar 

  • Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall-runoff models. Water Resour Res 27(9):2467–2471

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Information and Control 8(3):38–53

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, Canik Basari University, Samsun, Turkey

    Ozgur Kisi & Taner Mustafa Cengiz

Authors
  1. Ozgur Kisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taner Mustafa Cengiz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ozgur Kisi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kisi, O., Cengiz, T.M. Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region. Water Resour Manage 27, 3541–3553 (2013). https://doi.org/10.1007/s11269-013-0363-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-013-0363-7

Keywords

Search

Navigation