Arid
DOI10.1061/(ASCE)IR.1943-4774.0000343
Daily Evapotranspiration Modeling from Limited Weather Data by Using Neuro-Fuzzy Computing Technique
Karimaldini, Fatemeh1; Shui, Lee Teang1; Mohamed, Thamer Ahmed2; Abdollahi, Mohammadreza3; Khalili, Najmeh4
通讯作者Karimaldini, Fatemeh
来源期刊JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
ISSN0733-9437
EISSN1943-4774
出版年2012
卷号138期号:1页码:21-34
英文摘要

Evapotranspiration is an integral part of the hydrologic cycle and an important component in water resource development and management, especially in the arid and semiarid conditions such as those found in Iran in which water resources are limited. The standard Food and Agricultural Organization of the United Nations (FAO)-56 Penman-Monteith (PM) equation requires several meteorological inputs for estimating reference evapotranspiration (ETo) that are not usually available in most of the stations. This paper investigates the potential of the adaptive neuro-fuzzy computing technique (ANFIS) for daily reference evapotranspiration modeling under arid conditions from limited weather data. The gamma test technique is applied to find the best input combination and number of sufficient data points for the model calibration. The training and testing data sets are chosen on the basis of the K-fold method of cross-validation to obtain the optimal classifier. The estimates of ANFIS models are compared with calibrated FAO-56 reduced-set PM ETo approaches and some calibrated empirical ETo equations such as Hargreaves, Priestley-Tailor, Makkink, and Blaney-Criddle equations. The FAO-56 full-set PM is adopted as the reference ETo equation, and it is applied to calibrate other ETo equations and ANFIS models. The comparison results indicate that when similar meteorological inputs are used, the ANFIS models performed better than all the methods pursued. This fact strongly suggests using ANFIS technique as an accurate ETo estimator method even in the absence of complete weather data. The minimum required data to construct a good ANFIS model under arid conditions are the minimum and maximum air temperatures and wind speed data. DOI: 10.1061/(ASCE)IR.1943-4774.0000343. (C) 2012 American Society of Civil Engineers.


英文关键词Adaptive neuro-fuzzy computing technique (ANFIS) model Empirical formula Evapotranspiration
类型Article
语种英语
国家Malaysia ; Iran
收录类别SCI-E
WOS记录号WOS:000300438600004
WOS关键词GENETIC ALGORITHM ; INFERENCE SYSTEM ; SEMIARID REGIONS ; HEAT-FLUX ; EVAPORATION ; NETWORKS ; EQUATIONS ; RADIATION ; CROP
WOS类目Agricultural Engineering ; Engineering, Civil ; Water Resources
WOS研究方向Agriculture ; Engineering ; Water Resources
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/173731
作者单位1.Univ Putra Malaysia, Dept Agr & Biol Engn, Fac Engn, Serdang, Malaysia;
2.Univ Putra Malaysia, Dept Civil Engn, Fac Engn, Serdang, Malaysia;
3.Univ Putra Malaysia, Dept Elect & Elect Engn, Fac Engn, Serdang, Malaysia;
4.Ferdowsi Univ Mashhad, Dept Water Engn, Fac Agr, Mashhad, Iran
推荐引用方式
GB/T 7714
Karimaldini, Fatemeh,Shui, Lee Teang,Mohamed, Thamer Ahmed,et al. Daily Evapotranspiration Modeling from Limited Weather Data by Using Neuro-Fuzzy Computing Technique[J],2012,138(1):21-34.
APA Karimaldini, Fatemeh,Shui, Lee Teang,Mohamed, Thamer Ahmed,Abdollahi, Mohammadreza,&Khalili, Najmeh.(2012).Daily Evapotranspiration Modeling from Limited Weather Data by Using Neuro-Fuzzy Computing Technique.JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING,138(1),21-34.
MLA Karimaldini, Fatemeh,et al."Daily Evapotranspiration Modeling from Limited Weather Data by Using Neuro-Fuzzy Computing Technique".JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 138.1(2012):21-34.
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