Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification
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URI: http://hdl.handle.net/10835/15470
ISSN: 0022-1694
DOI: http://dx.doi.org/10.1016/j.jhydrol.2013.07.009
ISSN: 0022-1694
DOI: http://dx.doi.org/10.1016/j.jhydrol.2013.07.009
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Baudron, Paul; Alonso Sarría, Francisco; García Aróstegui, José Luis; Cánovas García, Fulgencio![Autoridad Universidad de Almería Autoridad Universidad de Almería](/themes/Mirage2/images/autoridades/autoridad.png)
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2013-07-15Resumen
Accurate identification of the origin of groundwater samples is not always possible in complex multilayered aquifers. This poses a major difficulty for a reliable interpretation of geochemical results. The problem is especially severe when the information on the tubewells design is hard to obtain. This paper shows a supervised classification method based on the Random Forest (RF) machine learning technique to identify the layer from where groundwater samples were extracted. The classification rules were based on the major ion composition of the samples. We applied this method to the Campo de Cartagena multi-layer aquifer system, in southeastern Spain. A large amount of hydrogeochemical data was available, but only a limited fraction of the sampled tubewells included a reliable determination of the borehole design and, consequently, of the aquifer layer being exploited. Added difficulty was the very similar compositions of water samples extracted from different aquifer layers. Moreover,...
Palabra/s clave
Multi-layer aquifer
Longscreen boreholes
Machine learning
Random Forest
Hydrogeochemistry
Hydrogeology