Prediction of whether precipitation based on decision tree

Prediction of precipitation is a complex phenomenon. The variation of precipitation is affected by temperature, pressure, wind, humidity, and dew point, etc. According to the respective atmospheric data ranging from 2000 to 2014 downloaded from ECMWF, this paper applies C4. 5 decision tree and random forest(RF) to predict the precipitation, and their accuracy reaches 82. 63% and 84. 36% respectively, which are much higher than SLIQ ( supervised learning in quest). However, the model-constructing consumes much more time than that of C4. 5 with the increase of data group.