An Entropy Based Elegant Decision Tree Classifier to Predict Precipitation

Prediction of precipitation is a necessary tool in meteorology. To date, it is technologically and scientifically a challenging task for scientists and researchers around the globe. Rainfall is a liquid form of precipitation that depends primarily on humidity, temperature, pressure, wind speed, dew point, and so on. Because rainfall depends on several parameters, its prediction becomes very complex. Approaches such as the back propagation model of neural network, linear regression, support vector machine, Bayesian networks, and fuzzy logic can be applied, but their rate of prediction is very low, which leads to unpredictable results. The present study focuses on the investigation of the application of decision trees, which is a data mining technique in the prediction of precipitation. This paper aims at improving the prediction of precipitation compared to Supervised Learning in Quest (SLIQ) decision trees, especially when datasets are large. Because SLIQ decision trees take more computational steps to find split points, they consume more time and thus cannot be applied to huge datasets. An elegant decision tree using entropy as an attribute selection measure is adopted in this study, which increases the accuracy rate and decreases the computation time. This approach provides an average accuracy of 76.12% with a reduction of 63% in computational time over SLIQ decision trees.