Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models
Climate 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...
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2023
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my.uniten.dspace-258452023-05-29T17:05:15Z Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models Panahi F. Ahmed A.N. Singh V.P. Ehtearm M. elshafie A. Torabi Haghighi A. 55368172500 57214837520 57211219633 57347979700 16068189400 56373737700 Climate 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; Seawater 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. � 2021 Elsevier Ltd Final 2023-05-29T09:05:15Z 2023-05-29T09:05:15Z 2021 Article 10.1016/j.jclepro.2021.129721 2-s2.0-85119491779 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119491779&doi=10.1016%2fj.jclepro.2021.129721&partnerID=40&md5=5bc89ca362db6a6002b4aba630f88788 https://irepository.uniten.edu.my/handle/123456789/25845 329 129721 Elsevier Ltd Scopus |
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Climate 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; Seawater |
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55368172500 |
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55368172500 Panahi F. Ahmed A.N. Singh V.P. Ehtearm M. elshafie A. Torabi Haghighi A. |
| format |
Article |
| author |
Panahi F. Ahmed A.N. Singh V.P. Ehtearm M. elshafie A. Torabi Haghighi A. |
| spellingShingle |
Panahi F. Ahmed A.N. Singh V.P. Ehtearm M. elshafie A. Torabi Haghighi A. Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| author_sort |
Panahi F. |
| title |
Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| title_short |
Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| title_full |
Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| title_fullStr |
Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| title_full_unstemmed |
Predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| title_sort |
predicting freshwater production in seawater greenhouses using hybrid artificial neural network models |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806426177934458880 |
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13.214268 |