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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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Synthetic data-driven explainable machine learning for groundwater salinity prediction in the Al-Qatif coastal aquifer of Saudi Arabia

2026Journal of Hydrology Regional StudiesJournalOpen access

10.1016/j.ejrh.2026.103258 Full text2 citations

Authors

6 authors across 4 institutions in 2 countries.

Ali AldreesFirst author
Abdulhayat M. JibrinCorresponding
Salisu Dan’azumi
- Bayero University Kano (NG)
Mohammad Al-Suwaiyan
Sani Abba
as printed: Sani I. Abba
Zaher Mundher YaseenSenior author

Fields

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

Groundwater and Isotope Geochemistrylevel 3 · 0.36
Hydrological Forecasting Using AIlevel 3 · 0.25
Groundwater and Watershed Analysislevel 3 · 0.08

Sources

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

openalexseen September 9, 2026
2026 · Journal of Hydrology Regional Studies · 2 citations
https://openalex.org/W7129376787

Synthetic data-driven explainable machine learning for groundwater salinity prediction in the Al-Qatif coastal aquifer of Saudi Arabia · Sankore