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

Application of statistical classification methods for predicting the acceptability of well-water... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Hydrogeology Journal Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Earth Sciences; Hydrogeology; Hydrology/Water Resources; Geology; Water Quality/Water Pollution; Geophysics/Geodesy; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
ISSN
1431-2174
eISSN
1435-0157
D.O.I.
10.1007/s10040-018-1727-0
Publisher site
See Article on Publisher Site

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.

Journal

Hydrogeology JournalSpringer Journals

Published: Jan 30, 2018

References

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