Prediction model of reference crop evapotranspiration based on extreme learning machine

Abstract: Reference crop evapotranspiration (ET0) is an essential parameter of water resource planning and management. Accurate estimation of ET0 becomes vital in planning and optimizing irrigation schedules and irrigation systems management. Numerous methods have been proposed for estimating ET0, among which Penman-Monteith (P-M) model recommended by Food and Agriculture Organization of the United Nations (FAO) in 1998 is the best one. FAO accepted the P-M model as the standard and sole equation for ET0 estimation since it provided the most accurate results across the world wherever in an arid or humid environment. But the main problems for computing ET0 by the P-M model are its complicated nonlinear process and requirements of many climatic variables. Thus, it is urgent to develop a simpler and more appropriate model in areas with limited data especially in developing countries like China. In the current study, the applicability of extreme learning machine (ELM) in ET0 modeling based on limited data was assessed in the humid environment in hilly area of central Sichuan, China. In addition, four climate-based models (Hargreaves, Priestley-Taylor, Makkink and Irmark-Allen) and the ELM model were tested against the P-M model to study their performance by using three commonly used criteria: root mean square error (RMSE), coefficient of determination (R2) and efficiency coefficient (Ens). From the statistical results, the ELM model performed well in expressing the nonlinear relationship between ET0 and meteorological factors; when based on temperature data, the ELM model performed better than Hargreaves model which is an empirical temperature-based model. When radiation and temperature data were introduced in the ELM model, the error decreased significantly, and it was much more accurate than the Priestley-Taylor, Makkink and Irmark-Allen model. It was found that the ELM model, which required maximum air temperature, minimum air temperature and sunshine duration input variables, had the best accuracy and was the optimal approach to estimate ET0 when the complete weather data required by the P-M model were not available. The further assessment of ELM was conducted and it was confirmed that the model could provide a powerful tool in estimating ET0 in the humid environment like hilly area of central Sichuan when lack of meteorological data. The research could provide a reference to accurate ET0 estimation in hilly area of central Sichuan.