An intelligent clustering method for devising the geochemical fingerprint of underground aquifers
| Autor: | A. Di Roma, Guido Sciavicco, Carmela Vaccaro, Estrella Lucena-Sánchez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Science (General) Earth science Climate change Sample (statistics) Aquifer Aquifer fingerprinting Evolutionary algorithms Geochemical fingerprinting 03 medical and health sciences Q1-390 0302 clinical medicine Computational statistics Cluster analysis Intelligent clustering H1-99 geography Multidisciplinary geography.geographical_feature_category Fingerprint (computing) Feature selection Ambientale Social sciences (General) 030104 developmental biology Principal component analysis 030217 neurology & neurosurgery Groundwater Geology Research Article |
| Zdroj: | Heliyon, Vol 7, Iss 5, Pp e07017-(2021) Heliyon |
| Popis: | Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, anchored in the recognition that geological processes leave behind physical, chemical and sometimes also isotopic patterns in the samples. Furthermore, the geochemical fingerprinting of natural cycles (water, carbon, soil and biota fingerprinting) are influenced by the anthropogenic impact and by the climate change. So, their monitoring is a tool of resilience and adaptation. 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 geological 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 a novel technique for geochemical fingerprinting of groundwater by means of which we are able to identify the exact aquifer from which a sample is extracted with a sufficiently high accuracy. Then, we experimentally prove that out method is sensibly more accurate than typical statistical approaches, such as principal component analysis, for this particular problem. Geochemical fingerprinting; Aquifer fingerprinting; Intelligent clustering; Feature selection; Evolutionary algorithms |
| Databáze: | OpenAIRE |
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