The Modeling of a River Impacted with Tailings Mudflows Based on the Differentiation of Spatiotemporal Domains and Assessment of Water–Sediment Interactions Using Machine Learning Approaches

The Modeling of a River Impacted with Tailings Mudflows Based on the Differentiation of Spatiotemporal Domains and Assessment of Water–Sediment Interactions Using Machine Learning Approaches

The modeling of metal concentrations in large rivers is complex because the contributing factors are numerous, namely, the variation in metal sources across spatiotemporal domains. By considering both domains, this study modeled metal concentrations derived from the interaction of river water and se...

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Bibliographic Details
Main Authors: João Paulo Moura, Fernando António Leal Pacheco, Renato Farias do Valle Junior, Maytê Maria Abreu Pires de Melo Silva, Teresa Cristina Tarlé Pissarra, Marília Carvalho de Melo, Carlos Alberto Valera, Luís Filipe Sanches Fernandes, Glauco de Souza Rolim
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Water
Subjects:
river
spatiotemporal domain
sediment source
metals
machine learning prediction
Online Access:https://www.mdpi.com/2073-4441/16/3/379

Internet

https://www.mdpi.com/2073-4441/16/3/379

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