A comparative study of RBF neural network and SVM classification techniques performed on real data for drinking water quality

The control and monitoring of drinking water is becoming more and more interesting because of its effects on human life. Many techniques were developed in this field in order to ameliorate this process control attending to rigorous follow-ups of the quality of this vital resource. Several methods were implemented to achieve this goal. In this paper, a comparative study of two techniques resulting from the field of the artificial intelligence namely: RBF neural network (RBF-NN) and support vector machine (SVM), is presented. 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 using real data, corresponding to the recognition rate, the training time, and the robustness, is carried out. To validate their functionalities, an application is presented.