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DOI	10.2166/nh.2016.205
	Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area
	Yin, Zhenliang; Wen, Xiaohu; Feng, Qi; He, Zhibin; Zou, Songbing; Yang, Linshan
发表日期	2017
ISSN	1998-9563
EISSN	2224-7955
卷号	48 期号:5
英文摘要	Accurate estimation of evapotranspiration is vitally important for management of water resources and environmental protection. This study investigated the accuracy of integrating genetic algorithm and support vector machine (GA-SVM) models using climatic variables for simulating daily reference evapotranspiration (ETo). The developed GA-SVM models were tested using the ETo calculated by Penman-Monteith FAO-56 (PMF-56) equation in a semi-arid environment of Qilian Mountain, northwest China. Eight models were developed using different combinations of daily climatic data including maximum air temperature (T-max), minimum air temperature (T-min), wind speed (U-2), relative humidity (RH), and solar radiation (R-s). The accuracy of the models was evaluated using root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (r). The results indicated that the GA-SVM models successfully estimated ETo with those obtained by the PMF-56 equation in the semi-arid mountain environment. The model with input combinations of Tmin, Tmax, U2, RH, and R-s had the smallest value of the RMSE and MAE as well as higher value of r (0.995) compared to other models. Relative to the performance of support vector machine (SVM) models and feed-forward artificial neural network models, it was found that the GA-SVM models proved superior for simulating ETo.
关键词	climatic variables genetic algorithm reference evapotranspiration modeling semi-arid mountain areas support vector machine
学科领域	Water Resources
语种	英语
WOS研究方向	Water Resources
来源期刊	HYDROLOGY RESEARCH
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/111918
作者单位	Chinese Acad Sci, Key Lab Ecohydrol Inland River Basin, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Yin, Zhenliang,Wen, Xiaohu,Feng, Qi,et al. Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area[J]. 中国科学院西北生态环境资源研究院,2017,48(5).
APA	Yin, Zhenliang,Wen, Xiaohu,Feng, Qi,He, Zhibin,Zou, Songbing,&Yang, Linshan.(2017).Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area.HYDROLOGY RESEARCH,48(5).
MLA	Yin, Zhenliang,et al."Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area".HYDROLOGY RESEARCH 48.5(2017).