Comparative study of conventional and artificial neural network-based ETo estimation models (original) (raw)

Abstract

Accurate estimation of reference crop evapotranspiration (ETo) is required for several hydrological studies and thus, in the past, a number of ETo estimation methods have been developed with different degree of complexity and data requirement. The present study was carried out to develop artificial neural network (ANN) based reference crop evapotranspiration models corresponding to the ASCE’s best ranking conventional ETo estimation methods (Jensen et al. ASCE Manual and Rep. on Engrg. Pract. no. 70, 1990). Among the radiation methods, FAO-24 radiation (or Rad) method for arid and Turc method for humid region, and among the temperature methods, FAO-24 Blaney–Criddle (or BC) method were studied. The ANN architectures corresponding to the above three less data-intensive methods were developed for four CIMIS (California Irrigation Management Information System) stations, namely, Davis, Castroville, Mulberry, and West Side Field station. The comprehensive ANN architecture developed by Kumar et al. (J Irrig Drain Eng 128(4):224–233, 2002) corresponding to Penman–Monteith (PM) ETo for Davis was also tried for the other three stations. Daily meteorological data for a period of more than 10 years (01 January 1990 to 30 June 2000) were collected from these stations and were used to train, test, and validate the ANN models. Two learning schemes, namely, standard back-propagation with learning rate of 0.2 and standard back-propagation with momentum having learning rate of 0.2 and momentum term of 0.95 were considered. ETo estimation performance of the ANN models was compared with the FAO-56 PM method. It was found that the ANN models gave better closeness to FAO-56 PM ETo than the best ranking method in each category (radiation and temperature). Thus these models can be used for ETo estimation in agreement with climatic data availability, when not all required climatic variables are observed.

Access this article

Log in via an institution

Subscribe and save

• Starting from 10 chapters or articles per month
• Access and download chapters and articles from more than 300k books and 2,500 journals
• Cancel anytime View plans

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

• Allen RG (1986) A Penman for all seasons. J Irrig Drain Eng 112(4):348–368
  Google Scholar
• Allen RG, Jensen ME, Wright JL, Burman RD (1989) Operational estimates of reference evapotranspiration. J Agron 81:650–662
  Google Scholar
• Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements.” Irrig. and Drain. Paper 56, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy
• Allen RG, Walter IA, Elliott R, Mecham B, Jensen ME, Itenfisu D, Howell TA, Snyder R, Brown P, Echings S, Spofford T, Hattendorf M, Cuenca RH, Wright JL, Martin D (2000) Issues, requirements and challenges in selecting and specifying a standardized ET equation.” Proc., 4th National Irrigation Symp., Am. Soc. Agric. Engrs. (ASAE), St. Joseph, MI, USA
• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a) Artificial neural networks in hydrology—I: preliminary concepts. J Hydrol Eng 5(2):115–123
  Article Google Scholar
• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b) Artificial neural networks in hydrology—II: hydrologic applications. J Hydrol Eng 5(2):124–137
  Article Google Scholar
• Businger JA (1956) Some remarks on Penman’s equations for the evapotranspiration. Neth J Agric Sci 4:77
  Google Scholar
• Chiew FHS, Kamaladassa NN, Malano HM, McMohan TA (1995) Penman–Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric Water Manag 28:9–21
  Article Google Scholar
• Daniel TM (1991) Neural networks applications in hydrology and water resources engineering. Proc Int Hydrol Water Resour Symp 3:797–802
  Google Scholar
• Dawson CW, Wilby RL (2001) Hydrological modeling using artificial neural network. Prog Phys Geogr 25(1):80–108
  Google Scholar
• DeSouza F, Yoder RE (1994) ET estimation in the north east of Brazil: Hargreaves or Penman–Monteith equation? Tech Paper, Int. Winter Meeting, Am. Soc. of Agric. Engrs. (ASAE), St. Joseph, MI
• Doorenbos J, Pruitt WO (1977) Guidelines for predicting crop water requirements. Irrig. and Drain. Paper No. 24, (2nd edn.), FAO, Rome, Italy
• Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530
  Article Google Scholar
• Itenfisu D, Elliot RL, Allen RG, Walter IA (2003) Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort. J Irrig Drain Eng 129(6):440–448
  Article Google Scholar
• Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE Manual and Rep. on Engrg. Pract. No. 70, ASCE, NY
• Khoob AR (2007) Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrig Sci doi: 10.1007/s00271-007-0090-z
• Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranpiration using artificial neural network. J Irrig Drain Eng 128(4):224–233
  Article Google Scholar
• Maier HR, Dandy GC (2000) Neural network for the prediction and forecasting of water resources variables: a review of modeling issues and application. Environ Model Softw 15:101–124
  Article Google Scholar
• Monteith JL (1965) The state and movement of water in living organisms. Proc., Evaporation and Environment, XIXth Symp., Soc. for Exp. Biol., Swansea. Cambridge University Press, NY, pp 205–234
• Odhiambo LO, Yoder RE, Yoder DC, Hines JW (2001) Optimization of fuzzy evapotranspiration model through neural training with input-output examples. Trans ASAE 44:1625–1633
  Google Scholar
• Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond 193:120–146
  Article CAS Google Scholar
• Penman HL (1963) Vegetation and hydrology. Tech. Communication No. 53, Commonwealth Bureau of Soils, Harpenden, England
• Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–221
  Article Google Scholar
• Tahir SA (1998) Estimating potential evapotranspiration using artificial neural network. Water and Land Resources Development and Management for Sustainable Use, Vol. II-A, 10th ICID Afro-Asian Regional Conference on Drainage, New Delhi, India
• Temesgen B, Eching S, Davidoff B, Frame K (2005) Comparison of some reference evapotranspiration equations for California. J Irrig Drain Eng 131(1):73–84
  Article Google Scholar
• Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting of reference evapotranspiration by artificial neural networks. J Irrig Drain Eng 129(6):454–457
  Article Google Scholar
• Turc L (1961) Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann Agron 12:13–14
  Google Scholar
• van Bavel CHM (1966) Potential evaporation: the combination concept and its experimental verification. Water Resour Res 2(3):455–467
  Article Google Scholar
• Walter IA, Allen RG, Elliott R, Jensen ME, Itenfisu D, Mecham B, Howell TA, Snyder R, Brown P, Echings S, Spofford T, Hattendorf M, Cuenca RH, Wright JL, Martin D (2000) ASCE’s standardized reference evapotranspiration equation. Proc., 4th national irrigation symp., Am. Soc. Agric. Engrs. (ASAE), St. Joseph, MI, USA
• Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo 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

Author information

Authors and Affiliations

1. SWCE, Vivekananda Institute of Hill Agriculture (Indian Council of Agricultural Research), Almora, 263 601, Uttarakhand, India
   M. Kumar
2. Centre for Flood Management Studies (Brahmaputra Basin), National Institute of Hydrology, Guwahati, 781 006, Assam, India
   A. Bandyopadhyay
3. Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, 721 302, West Bengal, India
   N. S. Raghuwanshi & R. Singh

Authors

1. M. Kumar
2. A. Bandyopadhyay
3. N. S. Raghuwanshi
4. R. Singh

Corresponding author

Correspondence toA. Bandyopadhyay.

Additional information

Communicated by A. Kassam.

Rights and permissions

About this article

Cite this article

Kumar, M., Bandyopadhyay, A., Raghuwanshi, N.S. et al. Comparative study of conventional and artificial neural network-based ETo estimation models.Irrig Sci 26, 531–545 (2008). https://doi.org/10.1007/s00271-008-0114-3

Download citation

• Received: 06 November 2007
• Accepted: 14 April 2008
• Published: 20 May 2008
• Issue date: September 2008
• DOI: https://doi.org/10.1007/s00271-008-0114-3

Keywords