Application of a Fuzzy Logic Based Methodology to Validate the Hydrochemical Characterization and Determining Seasonal Influence of a Watershed Affected by Acid Mine Drainage

Abstract

The Odiel River Basin, located in the Iberian Pyrite Belt (IPB), is heavily affected by acid mine drainage (AMD), which occurs when pyritic minerals from sulfide mining areas are exposed to atmospheric, hydrological or biological weathering. This paper presents a hydrochemical characterization of parameters in the Odiel River Basin by means of Fuzzy Logic and data mining methodologies to determine the seasonal influence of AMD in polluted waters that have not been used before for a basin in this environmental area. This technique was proven to be effective, providing results that could not be achieved by using classic statistics, because it allows us to characterize the different parameters separately and also their relationships in waters affected by AMD in a qualitative manner based on the antecedents and according to the conditions (rules) imposed by the consequents (in this case, the Fe(II) and accumulated rainfall over 30 days). Thus, it was possible to confirm that hydrochemistry is greatly affected by seasonal changes, with a higher pH in the wet season (up to 8.59) compared to 2.12, the minimum pH value reached in the dry season. Accordingly, higher concentrations of most of the metals were observed in the dry season (e.g., up to 4000 mg/L of Fe (II)), with the exception of the values found after the first rains that occur in the early fall. With the use of the Fuzzy Logic technique, it was observed that, during the wet season, lixiviates with a higher Fe content have higher metal concentrations, and in the dry season, the behavior is the opposite.

Identification of Salinization Processes in Coastal Aquifers Using a Fuzzy Logic and Data Mining Based Methodology. Study Case of Mediterranian Coastal Aquifer (Spain)

“…Finally, once the previous described values are determined, the graphical fuzzy rules are generated (Figure 3A). U: fuzzy partition matrix of X V : vector that is used to determine the cluster m: coefficient that measures the degree of concordance of the resulting groups, µik can take the values 0 ≤ µik ≤ 1, ||kx -vi || 2 A : is (xk -vi ) T A (xk -vi ) and is used for measuring distances PreFuRGe software (Aroba, 2003;Davila et al, 2021) analyses and classifies the provided dataset based on the above equation, so that each selected parameter as consequent, is fuzzy clustered in an optimum number of fuzzy clusters (Fukuyama and Sugeno, 1989). Then, each obtained fuzzy cluster is projected onto the antecedent space (Sugeno and Yasukawa, 1993), and the membership grade of the antecedents to each fuzzy cluster is determined.…”

“…This paper presents a new approach for characterizing the salinization processes in coastal aquifers. The proposed fuzzy logic methodology is based on the data mining computer tool Predictive Fuzzy Rules Generator -PreFuRGe- (Aroba, 2003), that has proved to be suitable for modelling the qualitative behaviour of complex systems (Grande et al, 2005;Grande et al, 2010, Davila et al, 2021. In the context of this research, the fuzzy logic have been used to address some problems such as: changes in the spatial variability of hydraulic parameters (Das et al, 2016), identification of quality indexes of groundwater (Vadiati et al, 2016, Guo et al, 2020, distribution of hydrochemical facies (Güler and Thyne, 2004;Güler et al, 2012, Agoubi et al, 2016, Rahbar et al, 2020, or prediction of a rock engineering classification system (Jalalifar et al, 2011).…”