Differentiation of Sahelian aquifers from chemical and isotopic composition using linear statistics and machine learning

Abstract

ABSTRACT 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.