中文摘要：

选取都安气象站5年（2008—2012年）的逐日气象数据,包括日最高气温、最低气温、平均风速、日照时数以及相对湿度5个气象要素的不同组合作为输入,并以FAO-56 Penman-Monteith法（FAO P-M）的计算结果作为标准值,采用基因表达式编程算法（GEP）及径向基函数网络算法（RBFNN）对参考作物腾发量ETo进行模拟计算,并将模拟结果与Hargreaves模型的计算结果进行比较,用决定系数R2和均方根误差RMSE作为评价指标。结果表明,GEP模型能够捕捉到ETo的变化,具有较强的适用性,与FAO P-M公式的计算值有很高的一致性。引入关键气象因子（气温和相对湿度）后,模型的决定系数R2达到0.914,均方根误差RMSE为0.240 mm/d。在相同输入情况下GEP模型计算精度高于RBFNN模型和Hargreaves模型,并建立了可以替代Hargreaves模型的GEP模型及缺少相对湿度RH时的GEP模型。结果表明,在缺乏相关气象因子时,可以利用GEP模型模拟ETo。

英文摘要：

Reference evapotranspiration （ETo） is a major component of the hydrological cycle. Accurate assessment of evapotranspiration is needed for water resources management and irrigation scheduling. The performance ability of gene-expression programming （GEP） and radical basis function neural network （RBFNN） was investigated for modeling ETo in weather station of Du＇an for a 5-year period （2008-- 2012）. The data set was comprised of daily maximum temperature, minimum temperature, sunshine duration and relative humidity, Which was employed for modeling ETo by using FAO - 56 Penman - Monteith equation as reference. GEP results were compared with RBFNN and Hargreaves models, and their performances were evaluated through determination coefficient （R2） and root mean square error RMsE. Based on the comparisons, GEP was found to perform better than RBFNN and Hargreaves models. The GEP model which can replace Hargreaves model and the GEP model without relative humidity were established. Statistically, GEP is an effectual modeling tool for successfully computing reference evapotranspiration.