SLGAS: Supervised Learning Using Gain Ratio as Attribute Selection Measure to Nowcast Snow/No-Snow

Abstract

Considering the weather nowcasting, which has no prospect of intervention, they cause the vital results in human life and animal life, accurate analysis and estimation of these variables is very important and crucial. There is an increased interest in nowcasting the bad weather conditions, among which presence of snow/no-snow is crucial, in order to fully capture the global atmospheric water cycle. This paper introduces an efficient decision tree algorithm named Supervised Learning using Gain Ratio as Attribute Selection measure (SLGAS), expanded to our previous algorithm Supervised Learning Using Entropy as Attribute Selection Measure (SLEAS), for the prediction of snow/no-snow using 31 international locations historical datasets, collected from various meteorological departments. The algorithm has been validated extensively with five performance measures namely accuracy, specificity, precision, dice and error rate respectively. Further, we compared our proposed method with the SLIQ and SLEAS decision tree algorithms in terms of the overall classification performance measures and it is clearly showing that the SLGAS algorithm is outperforming with an average accuracy of 85.92%, error rate of 14.07%.