Application of statistical classification methods for predicting the acceptability of well-water quality
| Autor: | Enrico Cameron, Fabio Stella, Giorgio Pilla |
|---|---|
| Přispěvatelé: | Cameron, E, Pilla, G, Stella, F |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
media_common.quotation_subject 0208 environmental biotechnology Aquifer 02 engineering and technology Well computer.software_genre 01 natural sciences Multivariate interpolation Contamination Groundwater quality Machine learning Earth and Planetary Sciences (miscellaneous) Quality (business) 0105 earth and related environmental sciences Water Science and Technology media_common geography geography.geographical_feature_category Hydrogeology Statistical classification 020801 environmental engineering Water resources Data mining Water quality computer Groundwater |
| Zdroj: | Hydrogeology Journal. 26:1099-1115 |
| ISSN: | 1435-0157 1431-2174 |
| Popis: | The application of statistical classification methods is investigated—in comparison also to spatial interpolation methods—for predicting the acceptability of well-water quality in a situation where an effective quantitative model of the hydrogeological system under consideration cannot be developed. In the example area in northern Italy, in particular, the aquifer is locally affected by saline water and the concentration of chloride is the main indicator of both saltwater occurrence and groundwater quality. The goal is to predict if the chloride concentration in a water well will exceed the allowable concentration so that the water is unfit for the intended use. A statistical classification algorithm achieved the best predictive performances and the results of the study show that statistical classification methods provide further tools for dealing with groundwater quality problems concerning hydrogeological systems that are too difficult to describe analytically or to simulate effectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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