干旱区生态环境知识资源中心(Arid): Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models

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DOI	10.1016/j.jclepro.2021.129721
	Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models
	Panahi, Fatemeh; Ahmed, Ali Najah; Singh, Vijay P.; Ehtearm, Mohammad; Elshafie, Ahmed; Haghighi, Ali Torabi
通讯作者	Ehtearm, M (corresponding author), Semnan Univ, Dept Water Engn, Semnan, Iran.
来源期刊	JOURNAL OF CLEANER PRODUCTION
ISSN	0959-6526
EISSN	1879-1786
出版年	2021
卷号	329
英文摘要	Freshwater production in seawater greenhouses (SWGH) is an important topic for decision-makers in arid lands. Since arid and semi-arid lands face water shortages, the use of SWGH helps farmers to supply water. This study proposed an integrated artificial neural network (ANN) model, namely, the ANN-antlion optimization algorithm (ANN-ALO), for predicting freshwater production in a seawater greenhouse. The width, length, and height of the evaporators and the roof transparency coefficient of the SWGH were used as the inputs of the models. The ability of ANN-ALO was benchmarked against the ANN-particle swarm optimization (ANN-PSO), ANN, and ANN-bat algorithms (ANN-BA). The novelties of the current study are the novel hybrid ANN models, the fuzzy reasoning concept for reducing the computational time, the comprehensive analysis of the uncertainty of the parameters and inputs, and the use of non-climate data. Comparing the models' performances in the test phase demonstrated that the ANN-ALO model performed best, with a Root Mean Square Error (RMSE) value that was 18%, 33%, and 39% lower than that of the ANN-BA, ANN-PSO, and ANN models, respectively. For the ANN model, the percent bias (PBIAS) value in the training stage was 0.20, whereas for the ANN-BA, ANN-PSO, and ANN-ALO models, it was 0.14, 0.16, and 0.12, respectively. This study also indicated that the width of the seawater greenhouse was the most important parameter for predicting freshwater production. Furthermore, the results suggested that an evaporator height of 2 m resulted in the highest predicted freshwater production for all the widths except 200 m. The lowest freshwater production for different widths occurred at an evaporator height of 3 m. The generalized likelihood estimation for uncertainty analysis indicated that the uncertainty of the input parameters was lower than that of the model parameters.
英文关键词	ANN Optimization algorithm Seawater greenhouse Water production
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000729483400007
WOS关键词	PARTICLE SWARM OPTIMIZATION ; MASS CONDENSATE FLUX ; BAT ALGORITHM ; ANTLION OPTIMIZATION ; DESALINATION ; CONSUMPTION ; SIMULATION ; SYSTEM
WOS类目	Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences
WOS研究方向	Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374147
作者单位	[Panahi, Fatemeh] Univ Kashan, Fac Nat Resources & Earth Sci, Kashan, Iran; [Ahmed, Ali Najah] Univ Tenaga Nasl UNITEN, Coll Engn, Inst Energy Infrastruct IEI, Dept Civil Engn, Kajang 43000, Selangor, Malaysia; [Singh, Vijay P.] Texas A&M Univ, Zachry Dept Civil & Environm Engn, Dept Biol & Agr Engn, College Stn, TX USA; [Ehtearm, Mohammad] Semnan Univ, Dept Water Engn, Semnan, Iran; [Elshafie, Ahmed] Univ Malaya UM, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia; [Elshafie, Ahmed] United Arab Emirates Univ, Natl Water & Energy Ctr, POB 15551, Al Ain, U Arab Emirates; [Haghighi, Ali Torabi] Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
推荐引用方式 GB/T 7714	Panahi, Fatemeh,Ahmed, Ali Najah,Singh, Vijay P.,et al. Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models[J],2021,329.
APA	Panahi, Fatemeh,Ahmed, Ali Najah,Singh, Vijay P.,Ehtearm, Mohammad,Elshafie, Ahmed,&Haghighi, Ali Torabi.(2021).Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models.JOURNAL OF CLEANER PRODUCTION,329.
MLA	Panahi, Fatemeh,et al."Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models".JOURNAL OF CLEANER PRODUCTION 329(2021).