  In recent years, there has been growing interest in using Precipitable Water
Vapor (PWV) derived from Global Positioning System (GPS) signal delays to
predict rainfall. However, the occurrence of rainfall is dependent on a myriad
of atmospheric parameters. This paper proposes a systematic approach to analyze
various parameters that affect precipitation in the atmosphere. Different
ground-based weather features like Temperature, Relative Humidity, Dew Point,
Solar Radiation, PWV along with Seasonal and Diurnal variables are identified,
and a detailed feature correlation study is presented. While all features play
a significant role in rainfall classification, only a few of them, such as PWV,
Solar Radiation, Seasonal and Diurnal features, stand out for rainfall
prediction. Based on these findings, an optimum set of features are used in a
data-driven machine learning algorithm for rainfall prediction. The
experimental evaluation using a four-year (2012-2015) database shows a true
detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy
of 79.6%. Compared to the existing literature, our method significantly reduces
the false alarm rates.
