Rainfall is one of the most considerable natural occurrences, which is
important for both human beings and living beings. Since the environment is changing
and there is a huge change in weather, it is noted that the rainfall cycles are also
varying and the earth’s temperature is increasing day-by-day. The changes in weather
conditions like humidity, pressure, wind speed, dew point and temperature affect the
agriculture, industry, production, and construction and also lead to floods and land-slides. Hence it is one of the important factors to be noted for human beings to keep
track of the natural occurrences in order to survive. In order to overcome these issues, a
system is required which is able to forecast and predict the rainfall using statistical
techniques which is the most popular tool in modern technology. This paper provides a
detailed survey and comparative analysis of various methodologies used in the
prediction of rainfall over multiple countries. Comparison is made in terms of various
performance measures: accuracy, precision, recall, RMSE, specificity, sensitivity,
MAE, F-Measure, ROC and RAE. Further, the drawbacks with existing approaches
applied so far in the prediction are discussed.
Keywords: Artificial Neural Networks, Classification Techniques, Decision Trees, Naïve Bayes, Rainfall, Random Forest, SVM.
