GA-BP模型模拟三峡库区参考作物蒸散量的适用性OA

Accurate estimation of reference evapotranspiration(ET0)is crucial for optimizing agricultural irrigation and managing water resources.However,the application of traditional empirical models is often limited due to missing meteorological data.This study investigated the applicability of a Genetic algorithm-optimized backpropagation neural network(GA-BP)model for ET0 simulation in the Three Gorges reservoir area.Using daily meteorological data from six observation stations between 1980 and 2023,16 kinds of GA-BP-based ET0 estimation models were developed and compared with three traditional empirical models:Hargreaves-Samani(H-S),Makkink and Irmak-Allen(I-A).The results showed that the GA-BP models significantly outperform traditional models across different regions.When only temperature and solar radiation data were used,the GA-BP2 model improved R² by 15.69%,reduced MAE by 35.36%,and decreased RMSE by 40.63%compared to the H-S model.With the addition of sunshine duration,the GA-BP6 model improved R² by 31.20%,reduced MAE by 27.33%and lowered RMSE by 23.47%compared to the Makkink model.After incorporating relative humidity,the GA-BP12 model outperformed the I-A model,with a 15.10%increase in R²,a 57.88%reduction in MAE,and a 43.92%decrease in RMSE.Therefore,the GA-BP model is recommended for ET0 estimation in the Three Gorges reservoir area,especially under conditions of limited meteorological data.