Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China

文献类型： 外文期刊

第一作者： Feng, Yu

作者： Feng, Yu;Gong, Daozhi;Cui, Ningbo;Zhao, Lu;Cui, Ningbo;Zhao, Lu;Cui, Ningbo;Hu, Xiaotao

作者机构：

关键词： Reference evapotranspiration;Extreme learning machine;Backpropagation neural networks;Wavelet neural networks;Southwest China

期刊名称：JOURNAL OF HYDROLOGY （ 影响因子：5.722； 五年影响因子：6.033 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Reference evapotranspiration (ET0) is an essential component in hydrological ecological processes and agricultural water management. Accurate estimation of ET0 is of importance in improving irrigation efficiency, water reuse and irrigation scheduling. FAO-56 Penman-Monteith (P-M) model is recommended as the standard model to estimate ET. Nevertheless, its application is limited due to the lack of required meteorological data. In this study, trained extreme learning machine (ELM), backpropagation neural networks optimized by genetic algorithm (GANN) and wavelet neural networks (WNN) models were developed to estimate ET0, and the performances of ELM, GANN,. WNN, two temperature-based (Hargreaves and modified Hargreaves) and three radiation-based (Makkink, Priestley-Taylor and Ritchie) ET0 models in estimating ET were evaluated in a humid area of Southwest China. Results indicated that among the new proposed models, ELM and GANN models were much better than WNN model, and the temperature based ELM and GANN models had better performance than Hargreaves and modified Hargreaves models, radiation-based ELM and GANN models had higher precision than Makkink, Priestley-Taylor and Ritchie models. Both of radiation-based ELM (RMSE ranging 0.312-0.332 mm d(-1), E-ns ranging 0.918-0.931, MAE ranging 0.260-0.300 mm d(-1)) and GANN models (RMSE ranging 0300-0.333 mm d(-1), E-ns ranging 0.916-0.941, MAE ranging 0.2580-0.303 mm d(-1)) could estimate ET0 at an acceptable accuracy level, and are highly recommended for estimating ET0 without adequate meteorological data. (C) 2016 Elsevier B.V. All rights reserved.

分类号： P33

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