Evapotranspiration Modeling Using Second-Order Neural Networks

This study introduces the utility of the second-order neural network (SONN) method to model the reference evapotranspiration (ET0) in different climatic zones of India. The daily climate data of minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation from 17 different locations in India were used as the inputs to the SONN models to estimate ET0 corresponding to the FAO-56 Penman-Monteith (FAO-56 PM) method. With the same inputs, for all 17 locations the first-order neural networks such as feed forward back propagation (FFBP-NN) models were also developed and compared with the SONN models. The developed SONN and FFBP-NN models were also compared with the estimates provided by the FAO-56 PM method. The performance criteria adopted for comparing the models were root-mean-squared error (RMSE), mean-absolute error (MAE), coefficient of determination (R2), and the ratio of average output to average target ET0 values (Rratio). Based on the comparisons, it is concluded that the SONN models applied successfully to model ET0 and performed better compared to the FFBP-NN models. This study also found that the SONN models yield better results using a fewer number of hidden neurons compared to FFBP-NN models. Better performance of SONN over FFBP-NN models suggest that SONN models can be used to estimate ET0 in different climatic zones of India.