干旱区生态环境知识资源中心(Arid): Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran

Arid

DOI	10.2166/wcc.2018.003
	Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran
	Seifi, Akram; Riahi, Hossien
通讯作者	Seifi, Akram
来源期刊	JOURNAL OF WATER AND CLIMATE CHANGE
ISSN	2040-2244
出版年	2020
卷号	11 期号:1 页码:217-240
英文摘要	In this study, a hybrid model of least square support vector machine-gamma test (LSSVM-GT) is proposed for estimating daily ETo under arid conditions of Zahedan station, Iran. Gamma test was used for selecting the best input vectors for models. The estimated ETo by LSSVM-GT model with different kernels of RBF, linear and polynomial, were compared with other hybrid approaches including ANN-GT, ANFIS-GT, and empirical equations. The gamma test revealed that climate variables of minimum and maximum air temperature and wind speed are the most important parameters. The LSSVM model performed better than the ANFIS and ANN models when similar meteorological input variables are used. Also, the performance of the three models of LSSVM, ANFIS, and ANN were better than the empirical equations such as Blaney-Criddle and Hargreaves-Samani. The RMSE, MAE, and R-2 for the best input vector by LSSVM were 0.1 mm day(-1), 0.13 mm day(-1), and 0.99, respectively. The threshold of relative absolute error of 95% predicted values by LSSVM, ANN, and ANFIS models were about 8.4%, 9.4%, and 24%, respectively. Based on the comparison of the overall performances, the developed LSSVM-GT approach is greatly capable of providing favorable predictions with high precision in arid regions of Iran.
英文关键词	adaptive neuro fuzzy inference system artificial neural network empirical equations gamma test least square support vector machine reference evapotranspiration
类型	Article
语种	英语
国家	Iran
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000526602600013
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; GLOBAL SOLAR-RADIATION ; WEATHER DATA ; REGRESSION ; RUNOFF ; SVM ; EVAPORATION
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315108
作者单位	Vali E Asr Univ, Dept Water Engn, Rafsanjan, Iran
推荐引用方式 GB/T 7714	Seifi, Akram,Riahi, Hossien. Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran[J],2020,11(1):217-240.
APA	Seifi, Akram,&Riahi, Hossien.(2020).Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran.JOURNAL OF WATER AND CLIMATE CHANGE,11(1),217-240.
MLA	Seifi, Akram,et al."Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran".JOURNAL OF WATER AND CLIMATE CHANGE 11.1(2020):217-240.