The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929589313339392 |
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| author | Peng, Guilong Sun, Senshan Xu, Zhenwei Du, Juxin Qin, Yangjun Sharshir, Swellam W. Kandel, A. W. Kabeel, A. E. Yang, Nuo |
| author_facet | Peng, Guilong Sun, Senshan Xu, Zhenwei Du, Juxin Qin, Yangjun Sharshir, Swellam W. Kandel, A. W. Kabeel, A. E. Yang, Nuo |
| contents | Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_12594 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning Peng, Guilong Sun, Senshan Xu, Zhenwei Du, Juxin Qin, Yangjun Sharshir, Swellam W. Kandel, A. W. Kabeel, A. E. Yang, Nuo Applied Physics Machine Learning Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study. |
| title | The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning |
| topic | Applied Physics Machine Learning |
| url | https://arxiv.org/abs/2307.12594 |