关键词: 京津冀地区, 参考作物蒸散量, BP神经网络模型, 麻雀算法

Abstract: In order to obtain the optimal simplified estimation model for reference crop evapotranspiration(ET0) in areas with a lack of meteorological data, the authors took the Beijing-Tianjin-Hebei region as the research area. Particle Swarm Algorithm(PSO), Genetic Algorithm(GA), Evolutionary Mind Algorithm(MEA), Sparrow Algorithm(SSA), and Artificial Fish Swarm Algorithm(AF) were used to optimize the BP model. Five optimization models: PSO-BP, GA-BP, MEA-BP, SSA-BP, AF-BP were constructed. These models were compared with BP model, random forest model(RF), wavelet neural network model(WNN), Hargreaves model(HS) and Droogrs-Allen model(DA). The results showed that: in different regions, the calculation accuracy of the five optimization models is significantly higher than the other models. SSA-BP model had the highest accuracy in different areas, with RMSE, R2, Ens and MAE of 0.297−0.402mm·d−1, 0.879−0.946, 0.862−0.940 and 0.210−0.300mm·d−1, respectively. The portability analysis results of the SSA-BP model showed that the model had strong generalization ability in the Beijing-Tianjin-Hebei region, and can achieve accurate estimation of ET0 from different stations. Thus, with only temperature data, the SSA-BP model can be used as a standard model for ET0 estimation in the Beijing- Tianjin-Hebei region.

Key words: Beijing-Tianjin-Hebei region, Reference crop evapotranspiration, BP neural network model, Sparrow algorithm