Optimization of an extreme learning machine model with the sparrow search algorithm to estimate spring maize evapotranspiration with film mulching in the semiarid regions of China

Abstract

Evapotranspiration (ET) is the crucial parameter of agricultural irrigation and the hydrological cycle. To obtain the optimal estimation model of ET with film-mulching for spring maize, the extreme learning machine model (ELM) optimized by sparrow search algorithm (SSA) was built. The ET results were compared with four machine learning models, including artificial bee colony algorithm optimized ELM model (ABC-ELM), particle swarm algorithm optimized ELM model (PSO-ELM), genetic algorithm optimized ELM model (GA-ELM), ELM model, and two empirical models, including the modified Shuttleworth-Wallace model (SW) and Priestley-Taylor model (PT). We evaluated the accuracy of different models using the root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), coefficient of efficiency (Ens) and GPI. The results showed that the SSA-ELM models show high accuracy under different input combinations in different growth periods. Throughout the growing season of spring maize, the slope of the fitting equation of the SSA-ELM9 model was 0.895. The RMSE, R2, Ens, MAE and GPI were 0.433 mm/d, 0.895, 0.895, 0.342 mm/d and 1.382, respectively. The SSA-ELM models showed the highest accuracy for ET estimation of spring maize in different growth periods, followed by PSO-ELM, ABC-ELM and GA-ELM models. The accuracy of the SSA-ELM models was better than that of the SW PT models. Therefore, the SSA-ELM model can estimate spring maize ET with film mulching.