Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China

Abstract

Summary Reference evapotranspiration (ET 0 ) is an essential component in hydrological ecological processes and agricultural water management. Accurate estimation of ET 0 is of importance in improving irrigation efficiency, water reuse and irrigation scheduling. FAO-56 Penman–Monteith (P–M) model is recommended as the standard model to estimate ET 0 . Nevertheless, its application is limited due to the lack of required meteorological data. In this study, trained extreme learning machine (ELM), backpropagation neural networks optimized by genetic algorithm (GANN) and wavelet neural networks (WNN) models were developed to estimate ET 0 , and the performances of ELM, GANN, WNN, two temperature-based (Hargreaves and modified Hargreaves) and three radiation-based (Makkink, Priestley–Taylor and Ritchie) ET 0 models in estimating ET 0 were evaluated in a humid area of Southwest China. Results indicated that among the new proposed models, ELM and GANN models were much better than WNN model, and the temperature-based ELM and GANN models had better performance than Hargreaves and modified Hargreaves models, radiation-based ELM and GANN models had higher precision than Makkink, Priestley–Taylor and Ritchie models. Both of radiation-based ELM (RMSE ranging 0.312–0.332 mm d −1 , E ns ranging 0.918–0.931, MAE ranging 0.260–0.300 mm d −1 ) and GANN models (RMSE ranging 0.300–0.333 mm d −1 , E ns ranging 0.916–0.941, MAE ranging 0.2580–0.303 mm d −1 ) could estimate ET 0 at an acceptable accuracy level, and are highly recommended for estimating ET 0 without adequate meteorological data.