Theoretical and Applied Climatology

, Volume 135, Issue 3–4, pp 945–958 | Cite as

Estimating wheat and maize daily evapotranspiration using artificial neural network

  • Nazanin Abrishami
  • Ali Reza SepaskhahEmail author
  • Mohammad Hossein Shahrokhnia
Original Paper


In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × KC equation (ET0 from Penman-Monteith equation and KC from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1C (8-4-2-1), with Tmax, Tmin, RHmax, RHmin, U2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5C (5-2-3-1), with Tmax, Tmin, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.


ANN Wheat evapotranspiration Maize evapotranspiration 


Funding information

This research supported in part by a research project funded by Grant no. 96-GR-AGR 42 of Shiraz University Research Council, Drought Research, Center of Excellence for On-Farm Water Management and Iran National Science Foundation (INSF).


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Nazanin Abrishami
    • 1
  • Ali Reza Sepaskhah
    • 1
    Email author
  • Mohammad Hossein Shahrokhnia
    • 1
  1. 1.Irrigation DepartmentShiraz UniversityShirazIran

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