A Nonparametric Cluster Method for Predicting Grounding Grid Corrosion Rate

Abstract

A nonparametric cluster classification prediction model was established aiming at small sample and parametric modeling of grounding grid corrosion rate. First of all, a grounding grid corrosion rate level classification strategy was used to reduced the complexity of the prediction model; secondly, a selfhelp method (Bootstrap) was utilized to produce Bootstrap subsets, avoiding small sample problem; finally, multiple weak classifiers were generated for all bootstrap subsets with nonparametric – KNN classification and the Adaboost method and were clustered into a strong classifier. The experimental result shows that, compared with the KNN classification method, corrosion rate levels resulting from the nonparametric cluster algorithm can be matched with the actual results better, and this model is suitable for the grounding grid corrosion rate prediction.