Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau
文献类型: 外文期刊
第一作者: Feng, Yu
作者: Feng, Yu;Gong, Daozhi;Mei, Xurong;Cui, Ningbo
作者机构:
关键词: evapotranspiration;extreme learning machine;generalized regression neural network;maize;modified dual crop coefficient approach
期刊名称:HYDROLOGY RESEARCH ( 影响因子:2.419; 五年影响因子:2.257 )
ISSN: 1998-9563
年卷期: 2017 年 48 卷 4 期
页码:
收录情况: SCI
摘要: Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (h(c)) were continuously measured during maize growing seasons of 2011-2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (K-c) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.
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