A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer

Abstract

Daily reference crop evapotranspiration (ET0) is crucial for precision irrigation management, yet traditional prediction methods struggle to capture its dynamic variations due to the complexity and nonlinearity of meteorological conditions. To address this, we propose an Improved Informer model to enhance ET0 prediction accuracy, providing a scientific basis for agricultural water management. Using meteorological and soil data from the Yingde region, we employed the Maximal Information Coefficient (MIC) to identify key influencing factors and integrated Residual Cycle Forecasting (RCF), Star Aggregate Redistribute (STAR), and Fully Adaptive Normalization (FAN) techniques into the Informer model. MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, soil temperature at 28–100 cm depth, and surface pressure as optimal features. Under the five-feature scenario (S3), the improved model achieved superior performance compared to Long Short-Term Memory (LSTM) and the original Informer models, with MAE reduced to 0.065 (LSTM: 0.637, Informer: 0.171) and MSE to 0.007 (LSTM: 0.678, Informer: 0.060). The inference time was also reduced by 31%, highlighting the enhanced computational efficiency. The Improved Informer model effectively captures the periodic and nonlinear characteristics of ET0, offering a novel solution for precision irrigation management with significant practical implications.