Modelling of Reference Evapotranspiration using Artificial Neural Network in Semi-arid Region of North Gujarat

Abstract

In order to efficiently manage the limited water resources in semi-arid regions, accurate quantification of irrigation water requirement of irrigated areas is deemed necessary. Since the irrigation water requirements of crops depend on reference evapotranspiration (ET₀ ), assessment of ET₀ is a key task in development of water resource management plans. This study examined effectiveness of the use of artificial neural networks (ANN) for the estimation of ET₀ using incomplete meteorological variables. These ANN models use daily meteorological data (maximum and minimum temperature, relative humidity, wind speed, and bright sunshine hours) as inputs, and provide ET₀ as outputs. Considering individual and possible combinations of these 5 input variables in combination with different number of hidden neurons (1 to 20), 31 model architectures were evaluated for their accuracy in estimating ET₀ . The results showed that the proper choice of neural network architecture allowed not only error minimization, but also strengthened the relationship between the dependent variable and the independent variables. The ANN architecture 5-11-1, having all five parameters as input, showed highest accuracy with R2 , NSE, RMSE, MAE as 0.98, 98.16%, 0.24 mm.day-1, 0.17 mm.day^-1 during training; and 0.98, 98.11%, 0.27 mm.day-1, 0.19 mm.day-1 during the testing period, respectively. Comparative performance of ANN models showed choice of input meteorological variables and model architecture are major determinants of ANN model performance and that the properly trained and tested ANN model can be used in estimation of ET₀ in the semi-arid regions of India.