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GA-BP模型模拟三峡库区参考作物蒸散量的适用性

苏浚镠 周明涛 陈博 杨佳佳 白思璐

中国农业气象2026,Vol.47Issue(3):344-352,9.
中国农业气象2026,Vol.47Issue(3):344-352,9.DOI:10.3969/j.issn.1000-6362.2026.03.003

GA-BP模型模拟三峡库区参考作物蒸散量的适用性

Applicability of the GA-BP Model in Simulating Reference Crop Evapotranspiration in the Three Gorges Reservoir Area

苏浚镠 1周明涛 1陈博 1杨佳佳 1白思璐1

作者信息

  • 1. 三峡大学土木与建筑学院,宜昌 443002
  • 折叠

摘要

Abstract

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.

关键词

GA-BP模型/参考作物蒸散量/三峡库区/ET0估算/气象数据缺失

Key words

GA-BP model/Reference crop evapotranspiration/Three Gorges reservoir area/ET0 estimation/Meteorological data deficiency

引用本文复制引用

苏浚镠,周明涛,陈博,杨佳佳,白思璐..GA-BP模型模拟三峡库区参考作物蒸散量的适用性[J].中国农业气象,2026,47(3):344-352,9.

基金项目

国家重点研发计划项目"流域尺度泥石流多措施协同韧性防控技术"(2024YFC3012702) (2024YFC3012702)

中国农业气象

1000-6362

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