Differentiation of Sahelian aquifers from chemical and isotopic composition using linear statistics and machine learning,
Auteur(s): Abdoul-Azize Barry, Suzanne Yameogo, Meryem Touzani, Samuel Nakolendoussé, Meryem Jabrane, Abdessamad Touiouine, Ismail Mohsine, Laurent Barbiero & Vincent Valles
Résumé

In Sahelian Africa, the characteristics of boreholes are often lost and, when several aquifers are present on
the same site, it is difficult to know which one is being tapped or is likely to be contaminated, which
hinders good management of the resource. In this study conducted on 153 wells distributed in the four
major aquifers of Burkina Faso, the variation in chemical composition within the aquifers is high
compared to that between the aquifers. In spite of this, treatment by linear statistical analysis and/or
machine learning allows the discrimination of the aquifers with a success rate of about 80%. The
introduction of water isotopes as an additional parameter and a dimensional reduction by principal
component analysis allowed a discrimination rate of 87.6% to be achieved. The pathway of water from
sedimentary to basement aquifers explains some of the confusion

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