灌溉排水学报2026，Vol.45Issue(2)：20-30,11.DOI:10.13522/j.cnki.ggps.2025284
基于群智能算法优化LSTM模型的参考作物蒸散量模拟研究
A proposed model for simulating daily reference crop evapotranspiration
摘要
Abstract
[Objective]The reference crop evapotranspiration(ET0)is an important parameter for irrigation scheduling and water resource management,but its estimation is often constrained by limited meteorological data.This paper proposes a method to bridge this technological gap.[Method]The study was conducted for the arid regions in Northwestern China.We divided the regions into four climatic zones:temperate continental arid zone,temperate continental high-temperature arid zone,plateau continental semi-arid zone,and temperate monsoon semi-arid zone.Daily meteorological data from 1961 to 2019 at eight representative weather stations in the region were used as baseline data for the model,and the ET0 calculated using the FAO-56 Penman-Monteith model served as the benchmark ET0.Three deep learning models,LSTM,GA-LSTM and PSO-LSTM,optimized the long short-term memory(LSTM)network hyperparameters using a genetic algorithm(GA)and particle swarm optimization(PSO),were developed to estimate ET0 by using air temperature,sunshine duration and relative humidity,either separately or in their combination,as the independent variables.We also compared the proposed models with three other empirical models:Priestley-Taylor,Hargreaves-Samani,and Romanenko model.[Result]The PSO-LSTM model was the most accurate,with its coefficient of determination(R2)and Nash-Sutcliffe efficiency(NSE)being 0.831-0.923 and 0.801-0.922,and RMSE,RRMSE,MAE and GPI being 0.476-0.866 mm/d,0.190-0.382,0.299-0.627 mm/d and 0.208-0.598,respectively.For areas with only temperature and radiation data,the PSO-LSTM1 model was the most accurate for all four climatic zones,with its R2 varying from 0.893 to 0.923.Intelligent optimization significantly improved the accuracy of LSTM,especially with PSO.[Conclusion]The PSO-LSTM model using temperature and radiation was found to be the most effective and reliable for estimating ET0 in arid regions in Northwestern China.It can be used to estimate ET0 in regions where meteorological data are limited.关键词
神经网络/遗传算法/粒子群优化算法/ET0模拟/西北干旱地区Key words
neural networks/genetic algorithm/particle swarm optimization/ET0 simulation/arid regions in Northwestern China分类
农业科技引用本文复制引用
李润童,邢立文,崔宁博,姜守政,王智慧,朱国宇,刘锦程,何清燕..基于群智能算法优化LSTM模型的参考作物蒸散量模拟研究[J].灌溉排水学报,2026,45(2):20-30,11.基金项目
国家重点研发计划项目(2022YFD1900805) （2022YFD1900805）
四川省科技计划项目(2024YFHZ0217,2024ZHCG0101) （2024YFHZ0217,2024ZHCG0101）
四川省基本科研业务费项目(2024JDKY0029-05) （2024JDKY0029-05）
中央高校基本科研业务费项目(20822041J4119) （20822041J4119）
国家自然科学基金项目(52309057) （52309057）
新平柑橘产业科技创新示范县创建项目(202304BT090019) （202304BT090019）
