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Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks
Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525301/ https://www.ncbi.nlm.nih.gov/pubmed/36180503 http://dx.doi.org/10.1038/s41598-022-20762-y |