Unsupervised Classification of Water Quality Using Artificial Intelligence: The Case of the Moulouya Wadi's Surface Waters (NE, Morocco)

Abstract

Due to its cruciality, water requires a high care to its physicochemical and microbiological properties to ensure the quality of several utilizations. The particles it carries are likely to be ingested, breathed, or come into contact with the skin. For the classification of the quality of surface water in the Moulouya River (NE, Morocco), this study presents many unsupervised classification methods. The overall quality of surface water in the Moulouya River in northeast Morocco was assessed using nine physicochemical parameters (pH, T°C, EC, O2-diss, NH4+, NO3-, SO42-, PO43-, and biological oxygen demand after 5 days (BOD5)) from March to August 2014. Over a 600-kilometer stretch, twenty-two sites were examined, from the river's source in the High Atlas to its mouth in the Mediterranean. During the first stage, three quality classes (excellent, good and poor) were defined by the calculation of the water quality index (WQI) and water quality evaluation system (QES-Water). In the second stage, the K-means algorithm, the fuzzy C-means algorithm and the self-organizing maps (SOM) of Kohonen were applied to the nine physicochemical parameters used as input variables for the model. The classification method used is capable of projecting high-dimensional data into a lower dimension, typically 2D. This nonlinear projection can be useful in classes’ analysis and their discovery. In terms of performance, the SOM classification showed very close results compared to the K-means and the fuzzy C-means algorithms, with only an insignificant difference across the three models, with SOM maps having a slight advantage.