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Artificial Neural Network for Predicting reference Evapotranspiration under Humid Region

Author Affiliations

  • 1Dept. of Soil and Water Engineering, CTAE, MPUAT, Udaipur, Rajasthan, India
  • 2Dept. of Soil and Water Engineering, CTAE, MPUAT, Udaipur, Rajasthan, India
  • 3Dept. of Soil and Water Engineering, CTAE, MPUAT, Udaipur, Rajasthan, India
  • 4Dept. of Soil and Water Engineering, CTAE, MPUAT, Udaipur, Rajasthan, India
  • 5Dept. of Agri. Statistics and computer application RCA, Udaipur, Rajasthan, India
  • 6Dept. of Soil and Water Engineering, CTAE, MPUAT, Udaipur, Rajasthan, India

Res. J. Recent Sci., Volume 5, Issue (11), Pages 25-31, November,2 (2016)

Abstract

Artificial neural network (AN) was used to assess the reference evapotranspiration under missing or limited climatic parameters as input variables. The climatic data from year 1991-2014 i.e. 24 years was used for study. The results indicated that temperature based ANN architecture 2-2-1 and 3-4-1 found suitable for estimation of reference evapotranspiration under humid conditions. For mass based ANN model 4-4-1 and 5-4-1 architectures found appropriate for forecasting of evapotranspiration. The ANN architecture 6-2-1 gives good outcome than other architectures when all climatic variables were considered in the input layers. The study found that the different ANN architectures may be used under limited or missing data conditions. Temperature based, mass based and combination based models can be used for estimation of evapotranspiration by selecting the ideal nodes in hidden layer.

References

  1. Khoshravesh Mojtaba, Mohammad Ali Gholami Sefidkouhi and Mohammad Valipour (2015)., Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments., Appl. Water Sci., DOI 10.1007/s13201-015-0368-x, 1-12.
  2. Govindaraju R. (2000)., Artificial neural networks in hydrology, II: Hydrological applications., Journal of Hydrological Engineering, ASCE, 5(2), 124-137.
  3. Kumar M., Raghuwanshi N.S., Singh R., Wallender W.W. and Pruitt W.O. (2002)., Estimating evapotranspiration using artificial neural network., J. of Irrig. Drain. Eng., ASCE, 128(4), 224-233.
  4. Sudheer K.P, Gosain A.K. and Ramasastri K.S. (2003)., Estimating actual evapotranspiration from limited climatic data using neural computing techniques., J. Irrig. Drain. Eng., ASCE, 129(3), 214-218.
  5. Trajkovic S. (2005)., Temperature based approaches for estimating reference evapotranspiration., J. Irrig. Drain. Engg., ASCE, 131(4), 316-323.
  6. Kisi O. (2006)., Evapotranspiration estimation using feed forward neural networks., Nordic Hydrology, 37(3), 247-260.
  7. Bhatt V. K., Tiwari A.K., Agnihotri Y. and Aggarwal R.K. (2007)., Inter-comparison of neural network and conventional techniques for estimating evapotranspiration., Hydrology Journal, 30(3-4), 19-30.
  8. Chauhan Seema and Shrivastava R.K. (2009)., Performance evaluation of reference evapotranspiration estimation using climate based methods and Artificial Neural Networks., Water Resour Manage., 23, 825-837.
  9. Willmott C. J. (1982)., Some comments on the evaluation of model performance., American Meteorological Soc., 63(11), 1309-1313.