ISPM: Improved Snow Prediction Model to Nowcast Snow/No-Snow

Abstract

Till date, many of the practioners, meteorologists, researchers, academicians, scientists across the globe proposed many methodologies and tools to nowcast snow/no-snow using satellite imagery, radar imagery, physical instruments, various algorithms, models and so on, adding to it some researchers estimated the amount of snow while some researchers detected the density of snow and few discriminated the differences between wet snow and dry snow. The main crux of the present research is to nowcast the presence of snow/no-snow more accurately by making use of historical weather datasets by adopting decision trees. In this paper, we are proposing a new algorithm Improved Snow Prediction Model (ISPM), an improvement to our earlier algorithms Snow Prediction Model (SPM), Improved Supervised Learning in Quest (ISLIQ), Supervised Learning using Gain Ratio as Attribute Selection Measure (SLGAS) and Supervised Learning using Entropy as Attribute Selection Measure (SLEAS). The ISPM algorithm out performs in terms of various performance measures like sensitivity, specificity, precision, dice, error rate and accuracy when compared with other decision tree models. The proposed method provides less computational complexity by evaluating the interval range, which significantly decreases the number of split points. Experimental results show that the ISPM algorithm scales up well to both large and small datasets with large number of attributes and class labels.