Application of statistical classification methods for predicting the acceptability of well-water quality
- Resource Type
- Authors
- Enrico Cameron; Fabio Stella; Giorgio Pilla
- Source
- Hydrogeology Journal. 26:1099-1115
- Subject
- 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
- Language
- ISSN
- 1435-0157
1431-2174
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.