Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including arithmetic mean of all the models (Ens1), average of...

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Detalles Bibliográficos
Autor principal: Onyutha, Charles
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534677/
https://www.ncbi.nlm.nih.gov/pubmed/36213032
http://dx.doi.org/10.1155/2022/7104752

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534677/
https://www.ncbi.nlm.nih.gov/pubmed/36213032
http://dx.doi.org/10.1155/2022/7104752

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