Genetic Optimization of Fuzzy Systems for the Classification of Treated Water Quality

The water quality problem is a world-wide challenge for human development. Traditionally, since the 1960’s water quality evaluation has been promoted through the application of various quality or pollution indexes. However, these indexes present excessive divergence and little complementarity that have not allowed to advance toward a unified system of globalized application due to the quantity, the range and the complexity of parameters. We propose a Takagi-Sugeno type fuzzy system to classify and make decisions about treated water reuse for direct or indirect contact activities. The fuzzy system design is based on expert knowledge, and considers guidelines established in mexican and international standards about pollution and water quality indexes. The coefficients in some rule consequents of the fuzzy system were determined by solving an optimization problem through a genetic algorithm. The classification performance of the fuzzy system was verified using real data of a water treatment plant through a leave-one-out cross validation and with an analysis of variance for the pollution indexes assessments.