Evaluation of the performances of ANN and SVM techniques used in water quality classification

The modern techniques in control and monitoring of drinking water, acquires a particular attention in the last few years. We attend more and more rigorous follow-ups of the quality of this resource, in order to master an effective control of the risks incurred for the public health. Several methods of control were implemented to meet this aim. In this paper, we present a comparative study of two techniques resulting from the field of the artificial intelligence namely: artificial neural networks (ANN), and support vector machines (SVM). Developed from the statistical learning theory, these methods display optimal training performances and generalization in many fields of application, among others the field of pattern recognition. Applied as classification tools, these techniques should ensure within a multi-sensor monitoring system, a direct and quasi permanent control of water quality. In order to evaluate their performances, a simulation corresponding to the recognition rate, the training time, and the robustness, is carried out. To validate their functionalities, an application of control of drinking water quality is presented.