A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification

Water quality is one of the major criteria for determining the planning and operation policies of water resources systems. In order to classify the quality of a water resource such as an aquifer, it is necessary that the quality of a large number of water samples be determined, which might be a very time consuming process. The goal of this paper is to classify the water quality using classification algorithms in order to reduce the computational time. The question is whether and to what extent the results of the classification algorithms are different. Another question is what method provides the most accurate results. In this regard, this paper investigates and compares the performance of three supervised methods of classification including support vector machine (SVM), probabilistic neural network (PNN), and k-nearest neighbor (KNN) for water quality classification. Using two performance evaluation statistics including error rate and error value, the efficiency of the algorithms is investigated. Furthermore, a 5-fold cross validation is performed to assess the effect of data value on the performance of the applied algorithms. Results demonstrate that the SVM algorithm presents the best performance with no errors in calibration and validation phases. The KNN algorithm, having the most total number and total value of errors, is the weakest one for classification of water quality data.