干旱区生态环境知识资源中心(Arid): COMPARATIVE STUDY OF POWERFUL PREDICTIVE MODELING TECHNIQUES FOR MODELING MONTHLY REFERENCE EVAPOTRANSPIRATION IN VARIOUS CLIMATIC REGIONS

Arid

	COMPARATIVE STUDY OF POWERFUL PREDICTIVE MODELING TECHNIQUES FOR MODELING MONTHLY REFERENCE EVAPOTRANSPIRATION IN VARIOUS CLIMATIC REGIONS
	Raza, Ali; Shoaib, Muhammad; Baig, Muhammad Azhar Inam; Ahmad, Shakil; Khan, Mudasser Muneer; Ullah, Muhammad Kaleem; Hashim, Sarfraz
通讯作者	Shoaib, M (corresponding author), Bahauddin Zakariya Univ, Dept Agr Engn, Multan, Pakistan.
来源期刊	FRESENIUS ENVIRONMENTAL BULLETIN
ISSN	1018-4619
EISSN	1610-2304
出版年	2021
卷号	30 期号:6B 页码:7490-7513
英文摘要	Estimation of reference evapotranspiration (ETo) is vibrantly required for estimating crop water requirement and budgeting irrigation scheduling. The beneficial use of water is of great importance due to shortage issues especially in developing countries like Pakistan. The Food and agricultural organization (FAO) developed a Peneman-Montieth (PM) method which can be globally considered as a standard method for estimation of ETo, but it requires numerous climatic data. Consequently, there is a need to find out the next best suitable method after PM method. The Multi-layer perceptron (MLP), Gene expression programing (GEP) and Radial basis function (RBF) were utilized to calculate ETo values. Monthly meteorological data of six different stations located in arid, semi-arid and humid regions of Pakistan covered from 1980 to 2015. Seventeen input combinations comprise of various climatic variables were developed to evaluate the impact on ETo. Of the available meteorological data, 70% was employed in training while remaining 30% used in testing process. The yielded values of the developed models were compared with the ETo estimated by PM method. The outcome of the study was also applied on some other climatic regions located in USA, New Zealand and China for numerous duration only three climatic parameters, namely, maximum temperature, mean relative humidity and wind velocity had a large positive effect on increasing the accuracy of estimating ETo. By comparing the eight performing indices, MLP among all the powerful predictive modeling techniques can also be considered as the superior alternative to the conventional methods in estimation of ETo.
英文关键词	Reference Evapotranspiration Climatic Zones Penman-Montieth method Multilayer perceptron Gene Expression Programming Radial Basis Function
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000670303700039
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; PENMAN-MONTEITH REFERENCE ; PAN EVAPORATION ; POTENTIAL EVAPOTRANSPIRATION ; EMPIRICAL EQUATIONS ; GENETIC ALGORITHM ; REGULARIZATION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368087
作者单位	[Raza, Ali] Jiangsu Univ, Dept Agr Engn, Zhenjiang, Jiangsu, Peoples R China; [Raza, Ali; Shoaib, Muhammad; Baig, Muhammad Azhar Inam] Bahauddin Zakariya Univ, Dept Agr Engn, Multan, Pakistan; [Ahmad, Shakil] Natl Univ Sci & Technol, NUST Inst Civil Engn, Islamabad, Pakistan; [Khan, Mudasser Muneer] Bahauddin Zakariaya Univ, Dept Civil Engn, Multan, Pakistan; [Ullah, Muhammad Kaleem] Univ Lahore, Dept Civil Engn, Lahore, Punjab, Pakistan; [Hashim, Sarfraz] MNSUA, Dept Agr Engn, Multan, Pakistan
推荐引用方式 GB/T 7714	Raza, Ali,Shoaib, Muhammad,Baig, Muhammad Azhar Inam,et al. COMPARATIVE STUDY OF POWERFUL PREDICTIVE MODELING TECHNIQUES FOR MODELING MONTHLY REFERENCE EVAPOTRANSPIRATION IN VARIOUS CLIMATIC REGIONS[J],2021,30(6B):7490-7513.
APA	Raza, Ali.,Shoaib, Muhammad.,Baig, Muhammad Azhar Inam.,Ahmad, Shakil.,Khan, Mudasser Muneer.,...&Hashim, Sarfraz.(2021).COMPARATIVE STUDY OF POWERFUL PREDICTIVE MODELING TECHNIQUES FOR MODELING MONTHLY REFERENCE EVAPOTRANSPIRATION IN VARIOUS CLIMATIC REGIONS.FRESENIUS ENVIRONMENTAL BULLETIN,30(6B),7490-7513.
MLA	Raza, Ali,et al."COMPARATIVE STUDY OF POWERFUL PREDICTIVE MODELING TECHNIQUES FOR MODELING MONTHLY REFERENCE EVAPOTRANSPIRATION IN VARIOUS CLIMATIC REGIONS".FRESENIUS ENVIRONMENTAL BULLETIN 30.6B(2021):7490-7513.