Application of Fuzzy Logic Techniques for Biogeochemical Characterization of Dams Affected by Acid Mine Drainage (AMD) Processes in the Iberian Pyrite Belt (IPB), Spain

Abstract

Water is one of the receptors media more affected by the environmental impacts, especially caused by mining sulfides exploitation. Acid mine drainage (AMD) is the main problem associated with these mining operations, producing extremely high impacts, and in many cases irreversible, still remaining nowadays. Diatoms, are the taxonomic algal group most used in environmental studies, to assess the water quality of rivers. From a monitoring perspective, the diagnosis of AMD contamination through the use of diatoms has proved to be an effective ecological tool to assess the impact and select the preventive and corrective measures more adequate to treat these impacted sites. In the present work, the existing relationships between biological and physicochemical indicators of acid mine drainage processes (AMD) in all the reservoirs affected by AMD in the Iberian Pyrite Belt (IPB) were studied through the use of fuzzy logic and data mining techniques that in contrast to the classic statistical treatments. The fuzzy rules show the relationship between biological and physical-chemical indicators, demonstrating the presence of a perfect correlation in all cases; thus, the numbers of species and pH have the same behavior, and inverse to that presented by the percentage of Pinnularia and the metallic charge and sulfates. These techniques improve the work considerably and make easier the knowledge of the involved processes, allowing a better discrimination of the diatoms responses to the stimuli caused by the hydrochemical changes imposed by the processes affecting water quality.