Performance evaluation of ANN and SVM multiclass models for intelligent water quality classification using Dempster-Shafer Theory

Water quality can be difficult to measure. Each water body can contain dramatically different levels of pollution. Water quality issues influence human and environmental health, so the more we monitor our water the better we will be able to recognize and prevent contamination problems. This paper presents the performance evaluation of Artificial Neural Network (ANN) and Support Vector Machines (SVM) classification techniques using Dempster-Shafer Theory of Evidence (DSTE) combination data fusion in monitoring of water quality. Dempster-Shafer Theory of Evidence (DSTE) was applied to increase the classification accuracy. This study involved the evaluation and interpretation of surface water quality data in Tilesdit dam (Algeria). It also allowed us to obtain more advanced information about water quality, and to design a monitoring network for this study area. The ANN and SVM which are two techniques for pattern classification have been widely used in many application areas such as water quality monitoring. These methods are binary classification techniques, but in some cases, such as pattern recognition, we need more than two classes. One of the suggested solutions for this difficulty is to split the problem into a set of binary classification before combining them. A multi-class problem using ANN and SVM is a typical example for solving the mentioned problem. The MLP network and the algorithm of SVM, one-against-all, are the most popular strategies for multi-class problems. In order to manage the conflict that results from using both approaches, the final decision is performed using DSTE's rule combination based on probabilistic output from both the two classifiers. In this work, four physicochemical parameters in 4 seasons during the period 2009-2011, located at Tilesdit dam in Bouira (Algeria), were selected for the purpose of this study, such as temperature, pH, conductivity and turbidity to supervise water quality. In order to evaluate their performances, a simulation using real dataset, corresponding to the recognition rate (training and test), is carried out. The results are compared to get the best performance evaluation of classification process which depends strongly on the choice of the excellent characteristics in training phase. The data fusion method improved significantly the performance of the intelligent approach in water quality classification. Moreover, the results demonstrated that the proposed procedure had great potential in water quality monitoring.