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Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models

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dc.citedby9
dc.contributor.authorPanahi F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSingh V.P.en_US
dc.contributor.authorEhtearm M.en_US
dc.contributor.authorelshafie A.en_US
dc.contributor.authorTorabi Haghighi A.en_US
dc.contributor.authorid55368172500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57211219633en_US
dc.contributor.authorid57347979700en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid56373737700en_US
dc.date.accessioned2023-05-29T09:05:15Z
dc.date.available2023-05-29T09:05:15Z
dc.date.issued2021
dc.descriptionClimate models; Decision making; Evaporators; Forecasting; Greenhouses; Mean square error; Neural networks; Particle swarm optimization (PSO); Uncertainty analysis; Arid lands; Artificial neural network modeling; Decision makers; Fresh Water; Hybrid artificial neural network; Optimization algorithms; Seawater greenhouse; Semi-arid lands; Uncertainty; Water production; Seawateren_US
dc.description.abstractFreshwater 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. � 2021 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo129721
dc.identifier.doi10.1016/j.jclepro.2021.129721
dc.identifier.scopus2-s2.0-85119491779
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85119491779&doi=10.1016%2fj.jclepro.2021.129721&partnerID=40&md5=5bc89ca362db6a6002b4aba630f88788
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25845
dc.identifier.volume329
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.titlePredicting freshwater production in seawater greenhouses using hybrid artificial neural network modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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