The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities

PURPOSE OF REVIEW: This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water. RECENT FINDINGS: We found 27 U.S. drinking water studies in the past ten years that used machine learning al...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Xindi C., Dai, Mona, Sun, Jennifer M., Sunderland, Elsie M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Metals and Health (TR Sanchez and M Tellez-Plaza, Section Editors)
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883334/
https://www.ncbi.nlm.nih.gov/pubmed/36527604
http://dx.doi.org/10.1007/s40572-022-00389-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883334/
https://www.ncbi.nlm.nih.gov/pubmed/36527604
http://dx.doi.org/10.1007/s40572-022-00389-x

Ejemplares similares