Regularization Learning of Trace Element Contamination Stemmed from Tailings Dam-Break
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Univ Tehran
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
An important practice in environmental risk management is assessing the consequences of heavy metal concentrations resulting from a mine dam tailing failure on soil, water, and trees. To ap-praise the extent of pollution, an effective classification is essential. In this study, trace element contamination is handled as a two-group classification problem and examined the performance of supervised regularization algorithms as spatial classifiers using imbalanced uncertain data. In addition to conventional shrinkage algorithms such as Ridge, the Lasso and Elastic-Net, the generalized t-statistic-based U-Lasso classifiers have been introduced and tested for mitigating such imbalances and adjusting weights for class distributions. The feature interpretation studies underlined that the most important indicator of the models is Zinc (Zn). The experimental stud-ies revealed that the Ridge classifier (l2penalty) outperforms the other models. Statistically, the U-Lasso models exhibited notable explanation capacity and their performances recorded close to the conventional shrinkage algorithms. The use of statistical learning-based classification ap-proach to appraise geo-environmental contamination under the conditions of natural variability and spatial uncertainty provides useful meta-data and reliable classification models.
Açıklama
Anahtar Kelimeler
Contamination, Tailings Dam Failure, Regularization, U-Lasso
Kaynak
Pollution
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
9
Sayı
3
