Abstract
Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, based on the idea that geological processes leave behind physical and chemical patterns in the samples. In recent years, computational statistics and artificial intelligence methods have started to be used to help the process of geochemical fingerprinting. In this paper we consider data from 57 wells located in the province of Ferrara (Italy), all belonging to the same aquifer group and separated into 4 different aquifers. The aquifer from which each well extracts its water is known only in 18 of the 57 cases, while in other 39 cases it can be only hypothesized based on geological considerations. We devise and test a novel automatic technique for geochemical fingerprinting of groundwater by means of which we are able to identify the exact aquifer from which a sample is extracted. Our initial tests returned encouraging results.
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Di Roma, A., Lucena-Sánchez, E., Sciavicco, G., Vaccaro, C. (2020). Towards Automatic Fingerprinting of Groundwater Aquifers. In: Valencia-García, R., Alcaraz-Marmol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2020. Communications in Computer and Information Science, vol 1309. Springer, Cham. https://doi.org/10.1007/978-3-030-62015-8_6
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