干旱区生态环境知识资源中心(Arid): Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)

Arid

DOI	10.2166/ws.2018.084
	Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)
	Mohammadrezapour, Omolbani 1; Piri, Jamshid 2; Kisi, Ozgur 3
通讯作者	Mohammadrezapour, Omolbani
来源期刊	WATER SUPPLY
ISSN	1606-9749
EISSN	1607-0798
出版年	2019
卷号	19 期号:2 页码:392-403
英文摘要	Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R-2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.
英文关键词	adaptive neuro-fuzzy inference system arid region climate parameters gene expression programming modeling support vector machine
类型	Article
语种	英语
国家	Iran ; Georgia
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000460773900005
WOS关键词	SCOUR DEPTH ; PREDICTION
WOS类目	Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/219303
作者单位	1.Univ Zabol, Dept Water & Soil, Zabol, Iran; 2.Univ Zabol, Dept Water Engn, Soil & Water Coll, Zabol, Iran; 3.Ilia State Univ, Fac Nat Sci & Engn, Tbilisi, Georgia
推荐引用方式 GB/T 7714	Mohammadrezapour, Omolbani,Piri, Jamshid,Kisi, Ozgur. Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)[J],2019,19(2):392-403.
APA	Mohammadrezapour, Omolbani,Piri, Jamshid,&Kisi, Ozgur.(2019).Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran).WATER SUPPLY,19(2),392-403.
MLA	Mohammadrezapour, Omolbani,et al."Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)".WATER SUPPLY 19.2(2019):392-403.