Hydrochemical analysis of groundwater using a tree-based model

Abstract

Hydrochemical indices are commonly used to ascertain aquifer characteristics, salinity problems, anthropogenic inputs and resource management, among others. This study was conducted to test the applicability of a binary decision tree model to aquifer evaluation using hydrochemical indices as input. The main advantage of the tree-based model compared to other commonly used statistical procedures such as cluster and factor analyses is the ability to classify groundwater samples with assigned probability and the reduction of a large data set into a few significant variables without creating new factors. We tested the model using data sets collected from headwater springs of the Jordan River, Israel. The model evaluation consisted of several levels of complexity, from simple separation between the calcium–magnesium–bicarbonate water type of karstic aquifers to the more challenging separation of calcium–sodium–bicarbonate water type flowing through perched and regional basaltic aquifers. In all cases, the model assigned measures for goodness of fit in the form of misclassification errors and singled out the most significant variable in the analysis. The model proceeded through a sequence of partitions providing insight into different possible pathways and changing lithology. The model results were extremely useful in constraining the interpretation of geological heterogeneity and constructing a conceptual flow model for a given aquifer. The tree model clearly identified the hydrochemical indices that were excluded from the analysis, thus providing information that can lead to a decrease in the number of routinely analyzed variables and a significant reduction in laboratory cost.