Fault diagnosis of WWTP based on improved support vector machines

Because of the characteristics of the abnormal data in waste water treatment plant (WWTP), such as the unbalanced distribution and cost sensitiveness of the fault classes data, a risk functional RWLOO(α) with weight coefficient based on leave-one-out errors was presented, and then Genetic Algorithms (GA) was used to globally optimize the risk functional RWLOO(α). In the optimization algorithm, the kernel function and some parameters of support vector machine (SVM) were optimized synchronously. The improved SVM was used to classify the dataset of WWTP, and the results have indicated that the misclassification rate of the improved SVM is 16.5% lower.