Estimating wheat and maize daily evapotranspiration using artificial neural network (original) (raw)

2018, Theoretical and Applied Climatology

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 (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RH max), minimum relative humidity (RH min), average relative humidity (RH ave), wind speed (U 2), 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 (ET C) values calculated by ET C = ET 0 × K C equation (ET 0 from Penman-Monteith equation and K C from FAO-56, ANN C) were used as outputs, and for the other five structures, the ET C values measured by weighing lysimeter (ANN M) 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»ANN 1 C (8-4-2-1), with T max , T min , RH max , RH min , U 2 , n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R 2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN 5 C (5-2-3-1), with T max , T min , Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ET C 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.