optimization of SVM parameters with hybrid PCA-PSO methods for water quality monitoring

Abstract

For the development of a water quality modeling classification, parameter optimization is important. In this research, in order to enhance the strength of the used approach, we propose a hybrid approach that combines SVM classifiers with PSO and PCA selection features. This is used for classifying the status of water quality with the Radial Basis Function (RBF) SVM kernel. To enhance the classification accuracy, PSO selects the best parameter for SVM. The problem of irrelevant data in the space of functions can be solved by PCA. A binary classification based on two water quality classes (Class I: upper, Class II: lower) is considered to be the problem. Datasets were obtained for training and testing over 5 years (2014-2018) from many samples in Tilsdit dam-Algeria, and are used in this situation. A simulation of the training time and recognition rate will be carried out in order to verify the efficiency of the method. The results obtained demonstrate that the proposed method had great potential for classifying water quality.