Comparing the Predictive Performance, Interpretability, and Accessibility of Machine Learning and Physically Based Models for Water Treatment
| Autor: | Graham A. Gagnon, Benjamin F. Trueman, William J. Raseman, Dewey W. Dunnington, Lindsay E. Anderson |
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| Rok vydání: | 2020 |
| Předmět: |
Langmuir
Computer science 0207 environmental engineering Mode (statistics) 02 engineering and technology General Medicine 010501 environmental sciences 01 natural sciences 6. Clean water Data set Modelling methods Coagulation (water treatment) Water treatment 020701 environmental engineering Biological system 0105 earth and related environmental sciences Interpretability |
| Zdroj: | ACS ES&T Engineering. 1:348-356 |
| ISSN: | 2690-0645 |
| DOI: | 10.1021/acsestengg.0c00053 |
| Popis: | Using an organic carbon removal data set (n = 500), we compared a physically based semiempirical coagulation model (Langmuir sorption-removal) and three ML modeling methods using quantitative (mode... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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