干旱区生态环境知识资源中心(Arid): Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

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DOI	10.1080/19942060.2019.1680576
	Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
	Yaseen, Zaher Mundher 1; Al-Juboori, Anas Mahmood 2; Beyaztas, Ufuk 3; Al-Ansari, Nadhir 4; Chau, Kwok-Wing 5; Qi, Chongchong 6; Ali, Mumtaz 7; Salih, Sinan Q.8; Shahid, Shamsuddin 1
通讯作者	Yaseen, Zaher Mundher ; Shahid, Shamsuddin
来源期刊	ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
ISSN	1994-2060
EISSN	1997-003X
出版年	2020
卷号	14 期号:1 页码:70-89
英文摘要	Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation - the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) - were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R-2 = .92), and with all variables as inputs at Station II (R-2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.
英文关键词	evaporation predictive model machine learning arid and semi-arid regions best input combination
类型	Article
语种	英语
国家	Malaysia ; Iraq ; Turkey ; Sweden ; Peoples R China ; Australia ; Vietnam
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000496623500001
WOS关键词	SUPPORT VECTOR REGRESSION ; WATER ; SOIL ; IMPLEMENTATION ; INTELLIGENCE ; COEFFICIENT ; SIMULATION ; INDEX ; AREA
WOS类目	Engineering, Multidisciplinary ; Engineering, Mechanical ; Mechanics
WOS研究方向	Engineering ; Mechanics
EI主题词	2020-01-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/312068
作者单位	1.Univ Teknol Malaysia, Fac Engn, Sch Civil Engn, Skudai 81310, Johor Bahru, Malaysia; 2.Univ Mosul, Dams & Water Resources Res Ctr, Mosul, Iraq; 3.Bartin Univ, Dept Stat, TR-74100 Bartin, Turkey; 4.Lulea Univ Technol, Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; 5.Hong Kong Polytech Univ, Dept Civil & Environm Engn, Kowloon, Hung Hom, Hong Kong, Peoples R China; 6.Cent S Univ, Sch Resources & Safety Engn, Changsha 410083, Hunan, Peoples R China; 7.Deakin Univ, Sch Informat Technol, Deakin SWU Joint Res Ctr Big Data, Geelong, Vic 3125, Australia; 8.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Yaseen, Zaher Mundher,Al-Juboori, Anas Mahmood,Beyaztas, Ufuk,et al. Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models[J],2020,14(1):70-89.
APA	Yaseen, Zaher Mundher.,Al-Juboori, Anas Mahmood.,Beyaztas, Ufuk.,Al-Ansari, Nadhir.,Chau, Kwok-Wing.,...&Shahid, Shamsuddin.(2020).Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models.ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS,14(1),70-89.
MLA	Yaseen, Zaher Mundher,et al."Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models".ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 14.1(2020):70-89.