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Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment

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Abstract

The objective of this study was to test an artificial neural network (ANN) for converting pan evaporation data (E p) to estimate reference evapotranspiration (ET0) as a function of the maximum and minimum air temperature. The conventional method that uses Pan coefficient (K p) as a factor to convert E p to ET0, is also considered for the comparison. The ANN has been evaluated under semi-arid conditions in Safiabad Agricultural Research Center (SARC) in the southwest of Iran, comparing daily estimates against those from the FAO-56 Penman–Monteith equation (PM), which was used as standard. The comparison shows that, the conventional method underestimated ET0 obtained by the PM method. The ANN method gave better estimates than the conventional method that requires wind speed and humidity data.

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References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. Guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56. FAO, Rome

    Google Scholar 

  • Bruton JM, Mcclendon RW, Hoogenboom G (2000) Estimating daily pan evaporation with artificial neural networks. Trans ASAE 43(2):491–496

    Google Scholar 

  • Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257

    Article  Google Scholar 

  • Cybenko G (1989) Approximation by superposition of a sigmoidal function. Math Control Signal Syst 2:303–314

    Article  Google Scholar 

  • Doorenbos J, Pruitt WO (1977) Guidelines for prediction of crop water requirements. FAO Irrig Drain. Paper No. 24, Rome

  • Gavilan P, Lorite IJ, Tornero S, Berengena J (2006) Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment. Agric Water Manage 81:257–281

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Irmak S, Haman D, Jones W (2002) Evaluation of class A pan coefficients for estimating reference evapotranspiration in humid location. J Irrig Drain Eng 128(3):153–159

    Article  Google Scholar 

  • Irmak S, Allen RG, Whitty EB (2003) Daily grass and alfalfa-reference evapotranspiration estimates and alfalfa-to-grass evapotranspiration ratios in Florida. J Irrig Drain Eng 129(5):360–370

    Article  Google Scholar 

  • Jensen ME (ed) (1974) Consumptive of water and irrigation water requirements. Irrig Drain Div ASCE, p 227

  • Jensen MC, Middleton JE, Pruitt WO (1961) Scheduling irrigation from pan evaporation. Circular 386, Washington Agricultural Experiment Station

  • 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 (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329(3–4):636–646

    Article  Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233

    Article  Google Scholar 

  • Odhiambo LO, Yoder RE, Hines JW (2001) Optimization of fuzzy evapotranspiration model through neural training with input-output examples. Trans ASAE 44(6):1625–1633

    Google Scholar 

  • Silva AF (2002) Previsão da evapotranspiração de referência utilizando redes neurais. Dissertação de Mestrado, Univ. Federal de Viçosa, Viçosa, Minas Gerais, Brazil

  • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 29(3):214–218

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Utset A, Farre I, Martinez-Cob A, Cavero J (2004) Comparing Penman–Monteith and Priestley–Taylor approaches as referenceevapotranspiration inputs for modeling maize wateruse under Mediterranean conditions. Agric Water Manage 66(3):205–219

    Article  Google Scholar 

  • Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernard S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng 133(2):83–89

    Article  Google Scholar 

Download references

Acknowledgments

The author would like to thank the anonymous reviewers for their valuable comments and suggestions which improved the content of the paper. This study is the partial work of Project No. 7351023/1/02 supported by University of Tehran and was done in Department of Irrigation and Drainage Engineering, Faculty of Agricultural Engineering, College of Abourayhan. The meteorological data were provided from Iran Meteorological Organization.

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Authors and Affiliations

  1. Irrigation and Drainage Engineering Department, Faculty of Agricultural Engineering, College of Abourayhan, University of Tehran, Abourayhan Campus, Pakdasht, Tehran, Iran

    Ali Rahimi Khoob

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  1. Ali Rahimi Khoob
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Correspondence to Ali Rahimi Khoob.

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Communicated by A. Kassam.

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Rahimi Khoob, A. Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment. Irrig Sci 27, 35–39 (2008). https://doi.org/10.1007/s00271-008-0119-y

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  • DOI: https://doi.org/10.1007/s00271-008-0119-y

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