In the present work, Acid Mine Drainage (AMD) processes in the Chorrito Stream, which flows into the Cobica River (Iberian Pyrite Belt, Southwest Spain) are characterized by means of clustering techniques based on fuzzy logic. Also, pH behavior in contrast to precipitation is clearly explained, proving that the influence of rainfall inputs on the acidity and, as a result, on the metal load of a riverbed undergoing AMD processes highly depends on the moment when it occurs. In general, the riverbed dynamic behavior is the response to the sum of instant stimuli produced by isolated rainfall, the seasonal memory depending on the moment of the target hydrological year and, finally, the own inertia of the river basin, as a result of an accumulation process caused by age-long mining activity.

Acid Mine Drainage is a water pollution type characterized by several topics such as high acidity, sulfate and heavy metal concentrations. One of the chemical characteristics is the absence of correlation between pH and conductivity, as could be expected. This last parameter is well correlated with other variables such as sulfate concentration and can be used as a field assessment. The absence of pH/conductivity correlation is largely discussed by several authors. In this work, the use of fuzzy logic algorithms in a large temporal database (over 20,000 records) has allowed us to study the "hidden" relation between them. This work finds this correlation, with some conditions such as the range of pH where it happens. Maybe the study of the usual range of pH values in previous studies has disturbed the correlation because of other chemical processes.

In this article, a set of clustering algorithms based on Fuzzy Logic and Data Mining are applied, allowing to obtain data in the form of linguistic rules and charts about the behaviour of the Tinto and Odiel river estuary (SW Spain) affected by Acid Mine Drainage (AMD). In order to provide researchers with no skills on data mining techniques an easy and intuitive interpretation, we have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data from the estuary water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics.

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Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees. This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart.