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Construction of a Novel Water Quality Classifier: A case study in the Sebou region

Mariam ELKHECHAFI, Hanaa ACHIMI, Aouatif AMINE, Youssfi El KETTANI


This study focuses on the evaluation of the water quality in the Sebou area, by applying the hybridization method of genetic algorithm (GA) by the algorithm of particle swarm optimization (PSO). This is initially transforming data from their raw format to a datamart ready to be interrogated by the statistical techniques that will be shown in the paper in order to specify the characteristics of the Sebou area. Iterative Principal Components Analysis (PCA) were then applied to resolve the problem of missing data. Then continue with the application of Support Vector Machine (SVM) polynomial classifier.
To be able to apply the (GA PSO) algorithm we need an objective function that serves as a criterion to determine the best solution to our problem, that’s why we opted for regression analysis(RA). The result achieved at the end of our study is represent by a classifier of water station according to a scale of 1 to 5 the most good quality to the worst. The novel water quality classifier is this helpful as supporting decision making for both the surface water and underground water.

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