Water Quality Analysis

The integration of water quality monitoring variables is essential in environmental decision making. Nowadays,

advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in

such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water

quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a

Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs

was estimated. Annual histograms of nine water quality variables were built withmonitoring data systematically

collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method

were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of

Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The

probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives

a larger number of agriculture, domestic, and industrial effluents. The results of the hybridmodel were compared

to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible

boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water

quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows

classify better the real water condition. The results of this study show that fuzzy inference systems integrated

to stochastic non-parametric techniques may be used as complementary tools in water quality indexing

methodologies.

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