Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

文献类型： 外文期刊

第一作者： Feng, Yu

作者： Feng, Yu;Peng, Yong;Cui, Ningbo;Feng, Yu;Peng, Yong;Cui, Ningbo;Gong, Daozhi;Zhang, Kuandi

作者机构：

关键词： Reference evapotranspiration;Temperature data;Extreme learning machine;Generalized regression neural network;Hargreaves model

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

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收录情况： SCI

摘要： Accurate estimation of reference evapotranspiration (ET0) is essential to agricultural water management. The present study developed two artificial intelligence models for daily ET0 estimation only with temperature data, including extreme learning machine (ELM) and generalized regression neural network (GRNN) in 6 meteorological stations of Sichuan basin, southwest China, and compared the proposed ELM and GRNN with the corresponding temperature-based Hargreaves (HG) model and its calibrated version considering FAO-56 Penman-Monteith ET0 as benchmark. Two data management scenarios were evaluated for estimation of ET0: (1) the models were trained/calibrated and tested using the local data of each station; and (2) the models were trained/calibrated using the pooled data from all the stations and tested in each station. In the first scenario, the results showed that the temperature-based ELM model provided the better estimation than the GRNN, HG and calibrated HG models, with average relative root mean square error (RRMSE) of 0.198, mean absolute error (MAE) of 0.267 mmid and Nash-Sutcliffe coefficient (NS) of 0.891, respectively. In the second scenario, GRNN model provided the most accurate results among the considered models, with average RRMSE of 0.194, MAE of 0.263 mm/d and NS of 0.895, respectively. Both of the temperature-based GRNN and ELM performed much better than the HG and calibrated HG models for the two scenarios, and the temperature-based GRNN and ELM models are appropriate alternatives for accurate estimation of ET0 for Sichuan basin of southwest China, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling. (C) 2017 Elsevier B.V. All rights reserved.

分类号： S

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