期刊
HYDROLOGY RESEARCH
卷 48, 期 4, 页码 1156-1168出版社
IWA PUBLISHING
DOI: 10.2166/nh.2016.099
关键词
evapotranspiration; extreme learning machine; generalized regression neural network; maize; modified dual crop coefficient approach
资金
- National Natural Science Foundation of China [51179194]
- National Key Technologies R&D Program of China [2015BAD24B01, 2012BAD09B01]
- Basic Science Research Foundation of China Central Government [BSRF201609]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据