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240429s2024 xx |||||o 00| ||eng c |
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|a 10.2166/wst.2024.092
|2 doi
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|a eng
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|a Alhamami, Ali Hussain
|e verfasserin
|4 aut
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|a Solar desalination system for fresh water production performance estimation in net-zero energy consumption building
|b A comparative study on various machine learning models
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 28.04.2024
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|a Date Revised 28.04.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
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|a This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods
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|a Journal Article
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|a Comparative Study
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|a environmental sustainability
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|a machine learning models water scarcity
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|a net-zero energy consumption
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|a renewable energy
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|a solar desalination
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|a water management
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|a Falude, Emmanuel
|e verfasserin
|4 aut
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|a Ibrahim, Ahmed Osman
|e verfasserin
|4 aut
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|a Dodo, Yakubu Aminu
|e verfasserin
|4 aut
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|a Daniel, Okpakhalu Livingston
|e verfasserin
|4 aut
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|a Atamurotov, Farruh
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Water science and technology : a journal of the International Association on Water Pollution Research
|d 1986
|g 89(2024), 8 vom: 18. Apr., Seite 2149-2163
|w (DE-627)NLM098149431
|x 0273-1223
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|g volume:89
|g year:2024
|g number:8
|g day:18
|g month:04
|g pages:2149-2163
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|u http://dx.doi.org/10.2166/wst.2024.092
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