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机器学习算法和Hargreaves模型在四川盆地ET0计算中的比较

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以四川盆地中部遂宁气象站2001-2010年逐日温度资料和大气顶层辐射(Ra)为输入参数,以FAO-56 Penman-Monteith (PM)模型计算的参考作物蒸散量(ET0)为标准,分别利用广义回归神经网络(GRNN)和小波神经网络(WNN)两种机器学习算法建立ET0模拟模型,并对GRNN、WNN和Hargreaves (HS1)与两种改进的Hargreaves(HS2和HS3)模型的ET0模拟效果进行对比分析,利用2011-2014年数据对各模型模拟精度进行验证,分析仅有温度资料时不同模型在四川盆地的适用性.结果表明:GRNN模型和WNN模型均具有较强的适用性,GRNN模型均方根误差(RMSE)、模型效率系数(Ens)和决定系数(R2)分别为0.395mm.d-1、0.924和0.902,WNN模型分别为0.401mm.d-1、0.911和0.901,且两种模型计算精度均高于HS1(1.05mm.d-1、0.885和0.334)、HS2(0.652mm.d-1、0.892和0.736)和HS3(0.550mm.d-1、0.881和0.812)模型.模型适用性验证进一步表明,GRNN和WNN模型在四川盆地西部和东部也具有较好的适用性,在输入参数中引入Ra能提高模型的模拟精度.因此,GRNN和WNN可以作为气象资料缺失条件下四川盆地ET0计算的推荐模型,且GRNN计算精度高于WNN,可优先选用.
Comparison of Machine Learning Algorithms and Hargreaves Model for Reference Evapotranspiration Estimation in Sichuan Basin
Reference evapotranspiration (ET0) is an essential component of agricultural water management,accurate estimation of ET0 is vital in irrigation scheduling.This study investigated the applicability of two machine learning algorithms,the generalized regression neural networks (GRNN) and wavelet neural networks (WNN),in modeling ET0 only with temperature data at Suining meteorological station,central Sichuan basin.The performances of GRNN and WNN models were compared with the empirical Hargreaves (HS 1) and two calibrated Hargreaves (HS2,HS3) models.From the results,the root mean square error (RMSE),model efficiency (Ens) and coefficient of determination(R2) were 0.395mm·d-1,0.924 and 0.902 for GRNN model,0.401mm.d-1,0.911 and 0.901 for WNN model,respectively.The performances of GRNN and WNN model were much better than HS 1,HS2 and HS3 model.A further performances evaluation of GRNN and WNN model was conducted,which manifested the better applicability of GRNN and WNN models in western and eastem Sichuan basin.

Reference evapotranspirationTemperature dataFAO-56 Penman-Monteith modelMachine learning algorithmHargreaves model

冯禹、崔宁博、龚道枝

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中国农业科学院农业环境与可持续发展研究所/农业部旱作节水农业重点实验室,北京100081

四川大学水力学与山区河流开发保护国家重点实验室/水利水电学院,成都610065

南方丘区节水农业研究四川省重点实验室,成都610063

参考作物蒸散量 温度资料 FAO-56 Penman-Monteith模型 机器学习算法 Hargreaves模型

农业部旱作节水农业重点实验室基金国家科技支撑计划项目四川省软科学研究计划项目

HZJSNY2015022015BAD24B012015ZR0157

2016

10.3969/j.issn.1000-6362.2016.04.005

中国农业气象
中国农业科学院农业环境与可持续发展研究所

中国农业气象

CSTPCDCSCD北大核心
影响因子：1.679
ISSN：1000-6362
年,卷(期)：2016.37(4)
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