Modelling the dynamics of the evapotranspiration process using genetic programming

Evapotranspiration constitutes one of the major components of the hydrological cycle and hence its accurate estimation is of vital importance to assess water availability and requirements. This study explores the utility of genetic programming (GP) to model the evapotranspiration process. An important characteristic of GP is that both the model structure and coefficients are simultaneously optimized. The method is applied in modelling eddy-covariance (EC)-measured latent heat (LE) as a function of net radiation (NR), ground temperature (GT), air temperature (AT), wind speed (WS) and relative humidity (RH). Two case studies having different climatic and topographic conditions are considered. The performance of the GP model is compared with artificial neural network (ANN) models and the traditional Penman-Monteith (PM) method. Results from the study indicate that both the datadriven models, GP and ANNs, performed better than the PM method. However, performance of the GP model is comparable with that of the ANN model. The GP-evolved models are dominated by NR and GT, indicating that these two inputs can represent most of the variance in LE. The results show that the GP-evolved equations are parsimonious and understandable, and are well suited to modelling the dynamics of the evapotranspiration process.