Contrast of evolution models for agricultural contaminants in ground waters by means of fuzzy logic and data mining

This work aims at contrasting, by means of a set of fuzzy logic- and data mining-based algorithms, the functioning model of a detritic aquifer undergoing overexploitation and nitrate excess input coming from strawberry and citrus intensive crops in its recharge zone. To provide researchers unskilled in data mining techniques with an easy and intuitive interpretation, the authors have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data mass from the water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics. M.L. de la Torre and J.A. Grande belongs to Water Resources and Quality Research Group. J.M. Andujar and J. Aroba belongs to Control and Robotics Research Group