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.

Cameron, E., Pilla, G., Stella, F. (2018). Application of statistical classification methods for predicting the acceptability of well-water quality. HYDROGEOLOGY JOURNAL, 26(4), 1099-1115 [10.1007/s10040-018-1727-0].

Application of statistical classification methods for predicting the acceptability of well-water quality

Stella, FA
2018

Abstract

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.
Articolo in rivista - Articolo scientifico
Contamination; Groundwater quality; Machine learning; Statistical classification; Well;
Contamination; Groundwater quality; Machine learning; Statistical classification; Well; Water Science and Technology; Earth and Planetary Sciences (miscellaneous)
English
2018
26
4
1099
1115
none
Cameron, E., Pilla, G., Stella, F. (2018). Application of statistical classification methods for predicting the acceptability of well-water quality. HYDROGEOLOGY JOURNAL, 26(4), 1099-1115 [10.1007/s10040-018-1727-0].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/199428
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