Unsupervised learning on Ghana soil data identifies a small set of anomalous heavy metal samples that match elevated health risk indices.
Health risks from persistent heavy metal contamination in crops and water at an abandoned naturally revegetated galamsey site in ghana
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment
Unsupervised learning on Ghana soil data identifies a small set of anomalous heavy metal samples that match elevated health risk indices.