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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Assessing the Suitability of Boosting Machine-Learning Algorithms for Classifying Arsenic-Contaminated Waters: A Novel Model-Explainable Approach Using SHapley Additive exPlanations

2022WaterJournalOpen access

10.3390/w14213509 Full text15 citations

Authors

3 authors across 2 institutions in 2 countries.

Bemah IbrahimFirst author
- University of Mines and Technology (GH)
Anthony Ewusi
- University of Mines and Technology (GH)
Isaac AhenkorahSenior authorCorresponding

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Arsenic contamination and mitigationlevel 3 · 0.98
Hydrological Forecasting Using AIlevel 3 · 0.98
Water Quality and Pollution Assessmentlevel 3 · 0.95

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 5, 2026
2022 · Water · 15 citations
https://openalex.org/W4309090111

Assessing the Suitability of Boosting Machine-Learning Algorithms for Classifying Arsenic-Contaminated Waters: A Novel Model-Explainable Approach Using SHapley Additive exPlanations · Sankore