Assessment the performance of classification methods in water quality studies, A case study in Karaj River.
To show the performance of classification methods in water quality studies, linear discriminant, and Naïve Bayesian classification methods were applied at nine sampling stations with respect to four parameters including COD, nitrite, nitrate, and total coliforms (selected from ten water quality variables) in Karaj River, Iran. To fulfill the goals of this study, the sampling stations were first separated into two groups using cluster analysis. Rural wastewater was the main source of pollution in the first group, whereas the quality of water in the second group has been degraded mainly by organic and agricultural pollution. In order to have an independent group against which the performance of other classification methods is considered, three cross-validation methods including twofold, leave-one-out, and holdout methods were utilized to retain an independent test set. The results of cross-validation for the linear discriminant analysis show that, except for the leave-one-out method with 11.1 % misclassification error, the overall performance has been the same as that of the training data set. Therefore, it has outperformed compared with that of Naïve Bayesian classification method. However, even though in situations where the correlation coefficient among the parameters is low, the latest method can offer the same performance as that of linear discriminant analysis as well. A sensitivity analysis was implemented using ten water quality variables (pH, COD, EC, TDA, turbidity, nitrate, nitrite, sulfate, TC, and FC) to find the most important variables in the classification of Karaj River showing that turbidity, next to COD, pH, nitrate, and sulfate, have had the most contribution in this field.
Department of Environmental Sciences, Faculty of Sciences, Shahid Rajaee Teacher Training University, Shahid Shabanloo Avenue, Lavizan, Tehran, Iran, msakizadeh@gmail.com.
Mohamad
M
Sakizadeh
D000383:Agriculture; D001499:Bayes Theorem; D016000:Cluster Analysis; D016002:Discriminant Analysis; D004784:Environmental Monitoring / Q000379:methods*; D007492:Iran; D045483:Rivers / Q000737:chemistry*; D062065:Wastewater / Q000737:chemistry; D014873:Water Pollutants / Q000032:analysis*; D060753:Water Quality
D062065:Wastewater; D014873:Water Pollutants