Bayesian machine learning and functional data analysis as a two-fold approach for the study of acid mine drainage events

Abstract

Acid mine drainage events have a negative influence on the water quality of fluvial systems affected by coal mining activities. This research focuses on the analysis of these events, revealing hidden correlations among potential factors that contribute to the occurrence of atypical measures and ultimately proposing the basis of an analytical tool capable of automatically capturing the overall behavior of the fluvial system. For this purpose, the hydrological and water quality data collected by an automated station located in a coal mining region in the NW of Spain (Fabero) were analyzed with advanced mathematical methods: statistical Bayesian machine learning (BML) and functional data analysis (FDA). The Bayesian analysis describes a structure fully dedicated to explaining the behavior of the fluvial system and the characterization of the pH, delving into its statistical association with the rest of the variables in the model. FDA allows the definition of several time-dependent correlations between the functional outliers of different variables, namely, the inverse relationship between pH, rainfall, and flow. The results demonstrate that an analytical tool structured around a Bayesian model and functional analysis automatically captures different patterns of the pH in the fluvial system and identifies the underlying anomalies.