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首页> 外文期刊>Nordic hydrology >Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau
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Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau

机译：黄土高原地区基于极限学习机和广义回归神经网络的玉米蒸散量估算。

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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 FT estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAD, and plant height (h_c) were continuously measured during maize growing seasons of 2011-2013. The meteorological data and crop data including LAI and h_c 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 ELMI, 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 K_c model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual K_c approach, but its estimation of maize ET was acceptable when only meteorological data were available.
机译：准确估算作物蒸散量（ET）对于干旱和半干旱农田的农业用水管理至关重要。本研究开发了极限学习机（ELM）和广义回归神经网络（GRNN）模型，用于中国黄土高原地区的玉米FT估算。在2011-2013年玉米生长期连续测量玉米ET，气象变量，叶面积指数（LAD）和株高（h_c），并利用2011年至2012年的气象数据和作物数据（包括LAI和h_c）来训练ELM。使用两种不同的输入组合对GRM和GRNN1的性能进行了比较，2013年将ELM和GRNN的性能与改良的双重作物系数（K_c）方法进行了比较。平方误差（RMSE）为0.221 mm / d，平均绝对误差（MAE）为0.203 mm / d，对于ELMI，NS为0.981，RMSE为0.225 mm / d，MAE为0.211 mm / d，对于GRNN1为NS 0.981仅使用气象数据作为输入的ELM2和GRNN2的性能比ELM1，GRNN1和改进的对偶K_c方法要差，但其对玉米ET的估计在仅当流星可获得病理学数据。

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  • 来源
    《Nordic hydrology》 |2017年第4期|1156-1168|共13页
  • 作者

    Yu Feng; Daozhi Gong; Xurong Mei; Ningbo Cui;

  • 作者单位

    State Engineering Laboratory of Efficient Water Use of Crops and Disaster Loss Mitigatior/MOA Key Laboratory for Dryland Agriculture,Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agriculture Sciences, Beijing, China;

    State Engineering Laboratory of Efficient Water Use of Crops and Disaster Loss Mitigatior/MOA Key Laboratory for Dryland Agriculture,Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agriculture Sciences, Beijing, China;

    State Engineering Laboratory of Efficient Water Use of Crops and Disaster Loss Mitigatior/MOA Key Laboratory for Dryland Agriculture,Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agriculture Sciences, Beijing, China;

    State Key Laboratory of Hydraulics and Mountain River Engineering,College of Water Resource and Hydropower,Sichuan University,Chengdu,China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    evapotranspiration; extreme learning machine; generalized regression neural network; maize; modified dual crop coefficient approach;

    机译：蒸散极限学习机;广义回归神经网络玉米;修正的双重作物系数法;

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