Information-Based Machine Learning for Tracer Signature Prediction in Karstic Environments

Karstic groundwater systems are often investigated by a combination of environmental or artificial tracers. One of the major downsides of tracer-based methods is the limited availability of tracer measurements, especially in data sparse regions. This study presents an approach to systematically evaluate the information content of the available data, to interpret predictions of tracer concentration from machine learning algorithms, and to compare different machine learning algorithms to obtain an objective assessment of their applicability for predicting environmental tracers. There is a large variety of machine learning approaches, but no clear rules exist on which of them to use for this specific problem. In this study, we formulated a framework to choose the appropriate algorithm for this purpose. We compared four different well-established machine learning algorithms (Support Vector Machines, Extreme Learning Machines, Decision Trees, and Artificial Neural Networks) in seven different karst springs in France for their capability to predict tracer concentrations, in this case SO42- and NO3-, from discharge. Our study reveals that the machine learning algorithms are able to predict some characteristics of the tracer concentration, but not the whole variance, which is caused by the limited information content in the discharge data. Nevertheless, discharge is often the only information available for a catchment, so the ability to predict at least some characteristics of the tracer concentrations from discharge time series to fill, for example, gaps or increase the database for consecutive analyses is a helpful application of machine learning in data sparse regions or for historic databases.